Journal articles: 'Manual Coding'

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Barnett, Ruthanna, Eva Codó, Eva Eppler, et al. "The LIDES Coding Manual." International Journal of Bilingualism 4, no. 2 (2000): 131–32. http://dx.doi.org/10.1177/13670069000040020101.

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White, Steven. "Coding Manual and Automated Audiometry." ASHA Leader 16, no. 6 (2011): 3. http://dx.doi.org/10.1044/leader.bml.16062011.3.

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Ruedin, Didier, and Laura Morales. "Estimating party positions on immigration: Assessing the reliability and validity of different methods." Party Politics 25, no. 3 (2017): 303–14. http://dx.doi.org/10.1177/1354068817713122.

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We provide a systematic assessment of various methods to position political parties on immigration, a policy domain that does not necessarily overlap with left–right and is characterized by varying salience and issue complexity. Manual and automated coding methods drawing on 283 party manifestos are compared – manual sentence-by-sentence coding using a conventional codebook, manual coding using checklists, automated coding using Wordscores, Wordfish and keywords. We also use expert surveys and the Comparative Manifesto Project (CMP), covering the main parties in Austria, Belgium, France, Ireland, the Netherlands, Spain, Switzerland and the United Kingdom, between 1993 and 2013. We find high levels of consistency between expert positioning, manual sentence-by-sentence coding and manual checklist coding and poor or inconsistent results with the CMP, Wordscores, Wordfish and the dictionary approach. An often-neglected method – manual coding using checklists – offers resource efficiency with no loss in validity or reliability.

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Lungu, Maria. "The Coding Manual for Qualitative Researchers." American Journal of Qualitative Research 6, no. 1 (2022): 232–37. http://dx.doi.org/10.29333/ajqr/12085.

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Wang, Song. "A Review of Research on Coding Methods for Open-ended Text Responses in Survey Questionnaires." Applied and Computational Engineering 106, no. 1 (2024): 93–98. http://dx.doi.org/10.54254/2755-2721/106/20241242.

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In fields such as social sciences and market research, open-ended questions can collect richer data information, but how to effectively count and analyse these text answers becomes a key issue. The study mainly explores the three coding methods of open-ended questions in questionnaires, including the definition, process, and application of manual coding, semi-automatic coding, and automatic coding. According to existing literature and data, manual coding has high flexibility and accuracy, but it is inefficient when processing large-scale data; semi-automatic coding combines manual coding and machine learning technology, which can improve efficiency while maintaining a certain degree of accuracy; automatic coding relies on natural language processing technology and deep learning models, which greatly improve coding efficiency, but there is a problem of insufficient accuracy when facing complex semantics. Future research can focus on improving the accuracy of automatic coding through deep learning, developing intelligent semi-automatic systems that reduce manual intervention, and incorporating real-time feedback mechanisms for continuous misappropriation.

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Wadia, Roxanne, Kathleen Akgun, Cynthia Brandt, et al. "Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–7. http://dx.doi.org/10.1200/cci.17.00069.

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Purpose To compare the accuracy and reliability of a natural language processing (NLP) algorithm with manual coding by radiologists, and the combination of the two methods, for the identification of patients whose computed tomography (CT) reports raised the concern for lung cancer. Methods An NLP algorithm was developed using Clinical Text Analysis and Knowledge Extraction System (cTAKES) with the Yale cTAKES Extensions and trained to differentiate between language indicating benign lesions and lesions concerning for lung cancer. A random sample of 450 chest CT reports performed at Veterans Affairs Connecticut Healthcare System between January 2014 and July 2015 was selected. A reference standard was created by the manual review of reports to determine if the text stated that follow-up was needed for concern for cancer. The NLP algorithm was applied to all reports and compared with case identification using the manual coding by the radiologists. Results A total of 450 reports representing 428 patients were analyzed. NLP had higher sensitivity and lower specificity than manual coding (77.3% v 51.5% and 72.5% v 82.5%, respectively). NLP and manual coding had similar positive predictive values (88.4% v 88.9%), and NLP had a higher negative predictive value than manual coding (54% v 38.5%). When NLP and manual coding were combined, sensitivity increased to 92.3%, with a decrease in specificity to 62.85%. Combined NLP and manual coding had a positive predictive value of 87.0% and a negative predictive value of 75.2%. Conclusion Our NLP algorithm was more sensitive than manual coding of CT chest reports for the identification of patients who required follow-up for suspicion of lung cancer. The combination of NLP and manual coding is a sensitive way to identify patients who need further workup for lung cancer.

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Chen, Hainan, Xiaowei Luo, and Zhenhua Zhu. "A worker posture coding scheme to link automatic and manual coding." Automation in Construction 125 (May 2021): 103630. http://dx.doi.org/10.1016/j.autcon.2021.103630.

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Daskalopoulos, Maria, Anastasia Kakouros, Nyryan Nolido, and Kathleen Malley-Morrison. "Elder Abuse Coding Manual: 09/05/06." Journal of Elder Abuse & Neglect 18, no. 2-3 (2006): 141–56. http://dx.doi.org/10.1300/j084v18n02_09.

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Lumbiganon, P. "Simplified icd coding manual for perinatal health." International Journal of Gynecology & Obstetrics 70 (2000): C7. http://dx.doi.org/10.1016/s0020-7292(00)81406-x.

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Yastori, Yastori. "Sosialisasi Kasus-Kasus Terkait Manual Coding dan Modern Coding Pada Dispute Claim." Journal of Community Engagement in Health 3, no. 2 (2020): 246–51. http://dx.doi.org/10.30994/jceh.v3i2.72.

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Kesesuaian kompetensi alumni dengan kebutuhan stakeholder merupakan hal yang krusial dalam perkembangan dan kemajuan ilmu pendidikan. Seperti perbedaan antara manual coding dengan modern coding menjadi salah satu permasalahan yang sering muncul bagi perekam medis dilapangan kerja. Ketepatan pengodean dapat berpengaruh terhadap pembayaran seperti kepada pihak pemberi asuransi. Tujuan sosialisasi yaitu memberikan gambaran contoh-contoh kasus terkait manual coding dan modern coding pada dispute claim dirumah sakit kepada perekam medis, dokter dan mahasiswa. Metode pengabdian kepada masyarakat dalam bentuk sosialisasi pada seminar online dengan menggunakan aplikasi Zoom pada Sabtu, 08 Agustus 2020 dengan 299 orang peserta. Sosialisasi dilakukan dalam bentuk penyampaian materi, diskusi dalam bentuk tanya jawab dan memberikan latihan contoh soal kepada peserta. Pada sosialisasi disampaikan contoh-contoh kasus manual coding dan modern coding pada dispute claim yang diambil dari beberapa rumah sakit di kota Padang, kemudian dijelaskan apa yang menyebabkan pengodean pada diagnosis dan tindakan tersebut mengalami dispute claim. Pada penyajian penjelasan dibatasi dari sudut pandang akademik yaitu berdasarkan ICD-10 dan ICD 9 CM oleh WHO. Setelah dilakukan sosialisasi, peserta seminar yang terdiri dari perekam medis, dokter, mahasiswa dan tenaga kerja dibidang pengodean lebih memahami penyebab dispute claim dari contoh kasus yang diberikan. Peningkatan pemahaman terkait ketepatan pengodean diagnosis dan tindakan dapat meminimalisir terjadinya dispute claim.

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Castillo, Louise IR, M. Erin Browne, Thomas Hadjistavropoulos, Kenneth M. Prkachin, and Rafik Goubran. "Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia." Journal of Rehabilitation and Assistive Technologies Engineering 7 (January 2020): 205566832095019. http://dx.doi.org/10.1177/2055668320950196.

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Introduction Technological advances have allowed for the estimation of physiological indicators from video data. FaceReader™ is an automated facial analysis software that has been used widely in studies of facial expressions of emotion and was recently updated to allow for the estimation of heart rate (HR) using remote photoplethysmography (rPPG). We investigated FaceReader™-based heart rate and pain expression estimations in older adults in relation to manual coding by experts. Methods Using a video dataset of older adult patients with and without dementia, we assessed the relationship between FaceReader’s™ HR estimations against a well-established Video Magnification (VM) algorithm during baseline and pain conditions. Furthermore, we examined the correspondence between the Facial Action Coding System (FACS)-based pain scores obtained through FaceReader™ and manual coding. Results FaceReader’s™ HR estimations were correlated with VM algorithm in baseline and pain conditions. Non-verbal FaceReader™ pain scores and manual coding were also highly correlated despite discrepancies between the FaceReader™ and manual coding in the absolute value of scores based on pain-related facial action coding of the events preceding and following the pain response. Conclusions Compared to expert manual FACS coding and optimized VM algorithm, FaceReader™ showed good results in estimating HR values and non-verbal pain scores.

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Law, M. J., M. M. White, and D. M. Lyle. "Computer or Manual Coding — Is There a Difference?" Australian Medical Record Journal 17, no. 3 (1987): 6–9. http://dx.doi.org/10.1177/183335838701700304.

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Advances in computer technology have now made it possible to code diseases and operations by computer as compared to the present manual system. In this study the speed and accuracy of a computerised coding system (3M Codefinder) was compared with the manual method on a sample of 90 medical records processed by two senior Medical Record Administrators (MRAs) with similar coding experience. The computer method took an average of 20.9 seconds longer to code a record than the manual method. A review of disease and operation codes indicated that both coders had an error rate of around 20%. Errors were mainly due to the coder's judgement, which resulted in under-coding or incorrect coding. However, a small proportion of computer coder's errors were caused by software problems, the most common being inconsistent prompting for the Place of Occurrence Code. Thus, the manual method has a moderate speed advantage over the computer while being comparable in terms of accuracy.

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Yue, Yongjie, Dong Liu, Yilin Lv, Junyi Hao, and Peixuan Cui. "A Practical Guide and Assessment on Using ChatGPT to Conduct Grounded Theory: Tutorial." Journal of Medical Internet Research 27 (May 14, 2025): e70122. https://doi.org/10.2196/70122.

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Generative large language models (LLMs), such as ChatGPT, have significant potential for qualitative data analysis. This paper aims to provide an early insight into how LLMs can enhance the efficiency of text coding and qualitative analysis, and evaluate their reliability. Using a dataset of semistructured interviews with blind gamers, this study provides a step-by-step tutorial on applying ChatGPT 4-Turbo to the grounded theory approach. The performance of ChatGPT 4-Turbo is evaluated by comparing its coding results with manual coding results assisted by qualitative analysis software. The results revealed that ChatGPT 4-Turbo and manual coding methods exhibited reliability in many aspects. The application of ChatGPT 4-Turbo in grounded theory enhanced the efficiency and diversity of coding and updated the overall grounded theory process. Compared with manual coding, ChatGPT showed shortcomings in depth, context, connections, and coding organization. Limitations and recommendations for applying artificial intelligence in qualitative research were also discussed.

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Ktari, Rim. "Coding Choices for Thematic Text Analysis: A Comparison of Manual Text Coding and Computer-assisted Coding." International Journal of the Humanities: Annual Review 8, no. 3 (2010): 1–12. http://dx.doi.org/10.18848/1447-9508/cgp/v08i03/42881.

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Wicks, David. "The Coding Manual for Qualitative Researchers (3rd edition)." Qualitative Research in Organizations and Management: An International Journal 12, no. 2 (2017): 169–70. http://dx.doi.org/10.1108/qrom-08-2016-1408.

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Saldaña, Johnny, and Leo A. Mallette. "Environmental Coding." Qualitative Inquiry 23, no. 2 (2016): 161–67. http://dx.doi.org/10.1177/1077800416679143.

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The purpose of this article is to describe a new coding and data analysis method for qualitative and evaluation researchers. We label this method Environmental Coding, an adaptation of Schmieder-Ramirez and Mallette’s (2007) text, The SPELIT Power Matrix: Untangling the Organizational Environment With the SPELIT Leadership Tool, into the frameworks for qualitative data analysis outlined in Saldaña’s (2016) The Coding Manual for Qualitative Researchers. Environmental Coding employs an eclectic combination of coding approaches in hermeneutic cycles to generate a multidimensional analysis of an environment’s culture and its drivers. This article uses one of Donald Trump’s initial candidate speeches for the 2016 U.S. presidential campaign to illustrate Environmental Coding in action.

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Jesus, Leonora F. de, Teody C. San Andres, and Christian B. San Andres. "Coding Methods for Enhanced Manual Data Transcripts: Contributing to Sustainable Development Goals (SDG)." Journal of Lifestyle and SDGs Review 4 (July 29, 2024): e01864. http://dx.doi.org/10.47172/2965-730x.sdgsreview.v4.n00.pe01864.

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Objective: The objective of this study is to explore the graduate school writers' intentions and behaviors related to adopting different manual coding methods. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. In this study, the Theory of Planned Behavior (TPB )stand out, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises Multi-phase Mixed-method; Frequency, Mean and Standard Deviation for quantitative was employed; Different Coding such as Emotion, In-vivo, Descriptive and Axial Coding Methods were also utilized for qualitative part. A probability sampling approach was employed. The Coding Manual for qualitative researchers by Saldana (2014) was adopted for instrumentation. Results and Discussion: The survey reveals a moderate level of understanding and proficiency in manual coding methods and theming. While respondents excel in foundational coding principles, there is room for improvement in using multiple coding methods and in vivo coding. The findings suggest that writers predominantly rely on traditional coding methods, revealing a gap in understanding and application of effective coding techniques and thematic analysis. Bridging the gap between traditional and alternative coding methods is crucial to advancing research practices in graduate school settin. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of Research. By addressing mentioned challenges, writers can improve the accuracy and depth of their data analysis, leading to more robust and meaningful research outcomes. In conclusion, there is a need to bridge the gap between the traditional coding method currently employed by graduate school writers and the alternative coding methods available. Additionally, providing training and support to enhance their understanding and application of coding methods and thematic analysis will contribute to the advancement of their research practices and ultimately lead to more comprehensive and insightful findings Originality/Value: This study contributes to the literature by providing valuable insights into the prevailing reliance on traditional coding methods among graduate school writers, thereby addressing Sustainable Development Goal (SDG) 4 on quality education. It highlights the associated challenges in effectively coding and theming qualitative data, revealing a gap between awareness and application of alternative coding techniques. The research highlight the urgent need for enhanced training and support in research practices, advocating for increased adoption of more effective coding methods. By improving the accuracy and depth of data analysis, this initiative not only aims to address current shortcomings but also seeks to advance qualitative research practices, fostering more robust and insightful research outcomes in academia

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Hober, Nicole, Tülay Dixon, and Tove Larsson. "Towards increased reliability and transparency in projects with manual linguistic coding." Corpora 18, no. 2 (2023): 245–58. http://dx.doi.org/10.3366/cor.2023.0284.

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Manually coded data form the basis of many of our analyses in corpus linguistics. It is thus imperative that we work towards increased reliability and enhanced transparency in our coding practices, since failing to do so may ultimately lead us to draw erroneous conclusions about language. Using spoken data from a study on adverb usage for illustration, this methods paper discusses some strategies for identifying threats to the reliability of our coding and offers suggestions for how to mitigate these and ensure that our coding can be assessed and replicated. The paper also includes suggestions for best practices for manual linguistic coding and concludes with a discussion of the benefits of such practices. With this paper, we expand on the ongoing discussions in the field on issues of reliability and transparency as they relate to manual coding. We argue that while tests of inter-rater reliability offer a helpful starting point, further steps are needed to ensure increased reliability and transparency.

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Lee, Sanguk, Siyuan Ma, Jingbo Meng, Jie Zhuang, and Tai-Quan Peng. "Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis." International Journal of Environmental Research and Public Health 19, no. 11 (2022): 6759. http://dx.doi.org/10.3390/ijerph19116759.

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Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.

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Astuti, Dwi Setyo. "Manual Coding : Model Pembelajaran Paling Sesuai bagi Pendidikan Biologi." EDUKATIF : JURNAL ILMU PENDIDIKAN 6, no. 3 (2024): 2060–67. http://dx.doi.org/10.31004/edukatif.v6i3.6648.

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Tuntutan keterampilan abad 21 memaksa proses pembelajaran berlangsung secara kritis, kreatif, penuh dengan kolaborasi dan aktif dalam komunikasi. New biology memberikan kriteria pembelajaran Biologi yang relevan dengan tuntutan abad 21, diantaranya adalah pemilihan model pembelajaran yang memberdayakan kemampuan berpikir tinggi meliputi PBL, PjBL, dan inquiry. Tujuan dari penelitian ini adalah untuk mengetahui model pembelajaran yang paling relevan untuk digunakan dalam pembelajaran Biologi. Subjek dalam penelitian ini adalah 20 dosen yang mengajar berbagai rumpun ilmu Biologi di Fakultas Keguruan dan Ilmu Pendidikan di beberapa Universitas Swasta di Jawa Tengah. Jenis penelitian adalah deskriptif kualitatif, teknik pengumpulan data berupa wawancara terbuka dan teknik analisis data menggunakan sistem manual coding. Hasil penelitian menunjukkan bahwa 60% dosen memilih menggunakan PBL dan menilai bahwa PBL paling relevan untuk pembelajaran Biologi. Sebanyak 30% menyatakan PjBL paling cocok untuk pembelajaran Biologi, serta 10% sisanya setuju bahwa model inquiry paling sesuai dengan karakteristik pembelajaran Biologi. Tidak ada satu model pembelajaran tertentu yang cocok untuk semua materi Biologi. Setiap materi Biologi memiliki karakteristik spesifik sehingga memerlukan penggunaan pendekatan, model dan metode pembelajaran yang berbeda pula. Namun demikian, dosen memiliki pertimbangan dan alasan tersendiri dalam pemilihan penggunaan model pembelajaran dan menganggap model tertentu menjadi yang paling ideal untuk pembelajaran Biologi

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Hall, P. A., and N. R. Lemoine. "Comparison of manual data coding errors in two hospitals." Journal of Clinical Pathology 39, no. 6 (1986): 622–26. http://dx.doi.org/10.1136/jcp.39.6.622.

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Priyanka, K., A. V. Narendran, S. B. Shanjaiy, M. Sivanesh, A. Vasanthbalan, and G. Yokesh. "Secure Coding Analysis Tool." Research and Reviews: Advancement in Cyber Security 2, no. 3 (2025): 46–52. https://doi.org/10.5281/zenodo.15583548.

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<em>Secure Code Analysis Tool is an extensive project that makes it easy and automatic to identify security vulnerabilities in software codebases. It is meant to make it easy to identify, explain, and report potential security threats in web, mobile, and API apps. It scans source code to identify typical security vulnerabilities like injection attacks, unsafe data management, and misconfigurations. It then produces human-friendly descriptions for the developers, enabling them to easily comprehend the type of vulnerabilities. The system also provides severity ratings and plots vulnerability distribution through a heatmap, giving an easy-to-use view of the security status of the code. The major goal of this project is to centralize and automate security analysis so that it is efficient and accurate in detecting vulnerabilities, thus eventually minimizing manual intervention and human faults in the development life cycle.</em> <em> </em>

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Kadlec, P., F. Puerner, and J. H. Hohnloser. "Coding Clinical Information: Analysis of Clinicians Using Computerized Coding." Methods of Information in Medicine 35, no. 02 (1996): 104–7. http://dx.doi.org/10.1055/s-0038-1634643.

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AbstractData are presented of a controlled experiment with a computerized browsing and encoding tool. Eighteen practicing clinicians extracted medical concepts from two narrative exercise cases using two approaches, traditional and computer-assisted use of ICD-9.Our results indicate that by using a computerized coding tool the completeness of coding can be improved by up to 55%, that by enforcing mandatory as opposed to optional modifier codes results in lower rates of incomplete coding (0 and 55%, respectively), higher rates of correct coding (41 to 92%) and no change in incorrect code, and that manual coding takes twice as long than coding with the help of the computerized coding tool. Clinicians need 59% more time for processing the whole set of codes than is suggested by the sum of individual codes. We conclude that the use of a computerized coding tool can save time and result in higher quality codes. However, the real time spent on coding may be underestimated when looking at individual coding times, instead of the whole task of processing a clinical scenario.

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He, Zhoushanyue, and Matthias Schonlau. "Automatic Coding of Text Answers to Open-Ended Questions: Should You Double Code the Training Data?" Social Science Computer Review 38, no. 6 (2019): 754–65. http://dx.doi.org/10.1177/0894439319846622.

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Open-ended questions in surveys are often manually coded into one of several classes (or categories). When the data are too large to manually code all texts, a statistical (or machine) learning model must be trained on a manually coded subset of texts. Uncoded texts are then coded automatically using the trained model. The quality of automatic coding depends on the trained statistical model, and the model relies on manually coded data on which it is trained. While survey scientists are acutely aware that the manual coding is not always accurate, it is not clear how double coding affects the classification errors of the statistical learning model. We investigate several budget allocation strategies when there is a limited budget for manual classification: single coding versus various options for double coding where the number of training texts is reduced to maintain the fixed budget. Under fixed budget, double coding improved prediction of the learning algorithm when the coding error is greater than about 20–35%, depending on the data. Among double-coding strategies, paying for an expert to resolve differences performed best. When no expert is available, removing differences from the training data outperformed other double-coding strategies. When there is no budget constraint and the texts have already been double coded, all double-coding strategies generally outperformed single coding. As under fixed budget, having an expert to solve disagreement in training texts improves accuracy most, followed by removing differences.

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Sahagún Padilla, Miguel Angel. "Review of Sardaña (2009) The Coding Manual for Qualitative Researchers." Quaderns de Psicologia 13, no. 1 (2011): 121. http://dx.doi.org/10.5565/rev/qpsicologia.904.

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Daroff, R. B. "The International Classification of Sleep Disorders: Diagnostic and Coding Manual." Neurology 41, no. 1 (1991): 160. http://dx.doi.org/10.1212/wnl.41.1.160.

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Simula, Brandy L. "Book Review: The Coding Manual for Qualitative Researchers, 3rd ed." Teaching Sociology 46, no. 2 (2018): 173–75. http://dx.doi.org/10.1177/0092055x18760362.

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Panzer, Stefan, Melanie Krueger, Thomas Muehlbauer, Attila J. Kovacs, and Charles H. Shea. "Inter-manual transfer and practice: Coding of simple motor sequences." Acta Psychologica 131, no. 2 (2009): 99–109. http://dx.doi.org/10.1016/j.actpsy.2009.03.004.

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Olagundoye, Olawunmi, Kees van Boven, Olufunmilola Daramola, Kendra Njoku, and Adenike Omosun. "Improving the accuracy of ICD-10 coding of morbidity/mortality data through the introduction of an electronic diagnostic terminology tool at the general hospitals in Lagos, Nigeria." BMJ Open Quality 10, no. 1 (2021): e000938. http://dx.doi.org/10.1136/bmjoq-2020-000938.

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BackgroundReliable information which can only be derived from accurate data is crucial to the success of the health system. Since encoded data on diagnoses and procedures are put to a broad range of uses, the accuracy of coding is imperative. Accuracy of coding with the International Classification of Diseases, 10th revision (ICD-10) is impeded by a manual coding process that is dependent on the medical records officers’ level of experience/knowledge of medical terminologies.Aim statementTo improve the accuracy of ICD-10 coding of morbidity/mortality data at the general hospitals in Lagos State from 78.7% to ≥95% between March 2018 and September 2018.MethodsA quality improvement (QI) design using the Plan–Do–Study–Act cycle framework. The interventions comprised the introduction of an electronic diagnostic terminology software and training of 52 clinical coders from the 26 general hospitals. An end-of-training coding exercise compared the coding accuracy between the old method and the intervention. The outcome was continuously monitored and evaluated in a phased approach.ResultsResearch conducted in the study setting yielded a baseline coding accuracy of 78.7%. The use of the difficult items (wrongly coded items) from the research for the end-of-training coding exercise accounted for a lower coding accuracy when compared with baseline. The difference in coding accuracy between manual coders (47.8%) and browser-assisted coders (54.9%) from the coding exercise was statistically significant. Overall average percentage coding accuracy at the hospitals over the 12-month monitoring and evaluation period was 91.3%.ConclusionThis QI initiative introduced a stop-gap for improving data coding accuracy in the absence of automated coding and electronic health record. It provides evidence that the electronic diagnostic terminology tool does improve coding accuracy and with continuous use/practice should improve reliability and coding efficiency in resource-constrained settings.

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Snyder, Nicole L., Amy Ising, and Anna E. Waller. "EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina." PLOS ONE 19, no. 4 (2024): e0295348. http://dx.doi.org/10.1371/journal.pone.0295348.

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Background The timeliness, accuracy, and completeness of data for firearm injury surveillance is crucial for public health surveillance efforts and informing injury prevention measures. While emergency department (ED) visit data can provide near real-time information on firearms injuries, there are concerns surrounding the accuracy of intent coding in these data. We examined whether emergency medical service (EMS) data provide more accurate firearm injury intent coding in comparison to ED data. Methods We applied a firearm injury definition to EMS encounter data in NC’s statewide syndromic surveillance system (NC DETECT), from January 1, 2021, through December 31, 2022. We manually reviewed each record to determine intent, and the corresponding manual classifications were compared to the injury cause codes entered in the EMS data and to ED visit records where EMS-ED record linkage was possible. We then calculated the sensitivity, specificity, positive and negative predictive values for each intent classification in SAS 9.4 using the manually reviewed intent classifications as the gold standard. Results We identified 9557 EMS encounters from January 1, 2021, through December 31, 2022 meeting our firearm injury definition. After removing false positives and duplicates, 8584 records were available for manual injury classification. Overall, our analysis demonstrated that manual and EMS injury cause code classifications were comparable. However, for the 3401 EMS encounters that could be linked to an ED visit record, sensitivity of the ED ICD-10-CM codes was low for assault and intentional self-harm encounters at 18.2% (CI 16.5–19.9%) and 22.2% (CI 16–28.5%), respectively. This demonstrates a marked difference in the reliability of the intent coding in the two data sources. Conclusions This study illustrates both the value of examining EMS encounters for firearm injury intent, and the challenges of accurate intent coding in the ED setting. EMS coding has the potential for more accurate intent coding than ED coding within the context of existing hospital-based coding guidance. This may have implications for future firearm injury research, especially for nonfatal firearm injuries.

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Ennser-Jedenastik, Laurenz, and Thomas M. Meyer. "The Impact of Party Cues on Manual Coding of Political Texts." Political Science Research and Methods 6, no. 3 (2017): 625–33. http://dx.doi.org/10.1017/psrm.2017.29.

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Do coders of political texts incorporate prior beliefs about parties’ issue stances into their coding decisions? We report results from a coding experiment in which ten coders were each given 200 statements on immigration that were extracted from election manifestos. Party labels in these statements were randomly assigned (including a control category without party cues). Coders were more likely to code a statement as pro-immigration if it was attributed to the Greens and less likely choose the anti-immigration category if it was attributed to the populist radical right. No effect was found for mainstream parties of the center-left and center-right. The results also suggest that coders resort to party cues as heuristics when faced with ambiguous policy statements.

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MacWhinney, Brian, Jenny A. Roberts, Evelyn P. Altenberg, and Madison Hunter. "Improving Automatic IPSyn Coding." Language, Speech, and Hearing Services in Schools 51, no. 4 (2020): 1187–89. http://dx.doi.org/10.1044/2020_lshss-20-00090.

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Purpose The Computerized Language ANalysis–Index of Productive Syntax (CLAN-IPSyn) system is designed to facilitate automatic computation of the IPSyn measure of productive child syntax. Roberts et al. (2020) conducted a thorough comparison of hand-generated and automatic scores on the Index of Productive Syntax (IPSyn) measure (Scarborough, 1990) and found a high level of error for CLAN-IPSyn. We report on the use of the Roberts et al. analysis to reduce and eliminate errors in CLAN-IPSyn, to improve its accuracy. Method Scores provided by manual and machine scoring of the 20 transcripts used in Roberts et al. (2020) were compared. Divergences in point assignment were examined and significant modifications made to the CLAN-IPSyn program to increase its accuracy. Conclusion The currently available, free version of CLAN at https://talkbank.org is now significantly more correct in terms of exemplars produced, and should assist clinicians and researchers in using the revised IPSyn (Altenberg et al. 2018).

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Basit, Tehmina. "Manual or electronic? The role of coding in qualitative data analysis." Educational Research 45, no. 2 (2003): 143–54. http://dx.doi.org/10.1080/0013188032000133548.

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Schaum, Kathleen D. "The National Correct Coding Initiative Policy Manual Is a Great Reference." Advances in Skin & Wound Care 38, no. 1 (2025): 8–9. https://doi.org/10.1097/asw.0000000000000258.

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Zhu, Mengxiao, Xin Wang, Xiantao Wang, Zihang Chen, and Wei Huang. "Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–23. http://dx.doi.org/10.1145/3686981.

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Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored ways to make sense of the complex processes involved in collaborative problem solving. However, encoding explicit behaviors into subskills within the frameworks of CPS skills is still a challenging task. Traditional studies have relied on manual coding to decipher behavioral data for CPS, but such coding methods can be very time-consuming and cannot support real-time analyses. Scholars have begun to explore approaches for constructing automatic coding models. Nevertheless, the existing models built using machine learning or deep learning techniques depend on a large amount of training data and have relatively low accuracy. To address these problems, this paper proposes a prompt-based learning pre-trained model. The model can achieve high performance even with limited training data. In this study, three experiments were conducted, and the results showed that our model not only produced the highest accuracy, macro F1 score, and kappa values on large training sets, but also performed the best on small training sets of the CPS behavioral data. The application of the proposed prompt-based learning pre-trained model contributes to the CPS skills coding task and can also be used for other CSCW coding tasks to replace manual coding.

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Bryant, Ashley Leak, Rachel Hirschey, Courtney Caiola, et al. "Refining an Oral Health Care Partner Intervention Using Behavior Change Techniques." Innovation in Aging 5, Supplement_1 (2021): 413. http://dx.doi.org/10.1093/geroni/igab046.1599.

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Abstract Following a pilot, we refined an oral health carepartner intervention for individuals with mild dementia (IMD). In this intervention, we use behavior change techniques (BCTs) to foster changes by carepartners including using new oral-care techniques and developing skills for using cueing and communications approaches to support behavior changes by IMD (duration and frequency of toothbrushing and oral-hygiene skills); thus, improving plaque and gingival indices. We describe our approach to refining the intervention manual including a) completing the self-paced BCT taxonomy course, b) developing a coding schema, c) coding the original manual for evidence of BCTs, and d) refining the manual to improve use of BCTS in the refined intervention. Our results detail how BCTs can be applied to refine and improve interventions. This research demonstrates the value in using BCTs for interventions to address how carepartners and IMD can collaborate to improve oral hygiene care.

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Chan, Chung-hong, Rainer Freudenthaler, and Philipp Müller. "Developing a synthetic news corpus to validate generic frame detection methods." Studies in Communication and Media 13, no. 1 (2024): 101–24. http://dx.doi.org/10.5771/2192-4007-2024-1-101.

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Frames are a central concept in communication research. Based on our literature review, we propose that frame identification is an act of identifying selected reality and communicative intention. We then highlight the conceptual and methodological issues of frame identification using computational methods. To avoid the correlation between topics and frames, we provide a synthetic dataset for evaluating frames found in multi-topical news content, using the detection of generic frames as a test case. With this dataset, for the first time, we benchmark manual coding and various automatic and semi-supervised methods. Based on the preliminary benchmark results, this study provides evidence that generic frame identification using both manual coding and automatic methods might not be accurate.

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Neumayr, Thomas, Mirjam Augstein, Johannes Schönböck, Sean Rintel, Helmut Leeb, and Thomas Teichmeister. "Semi-automated analysis of collaborative interaction: are we there yet?" Proceedings of the ACM on Human-Computer Interaction 6, ISS (2022): 354–80. http://dx.doi.org/10.1145/3567724.

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In recent years, research on collaborative interaction has relied on manual coding of rich audio/video recordings. The fine-grained analysis of such material is extremely time-consuming and labor-intensive. This is not only difficult to scale, but, as a result, might also limit the quality and completeness of coding due to fatigue, inherent human biases, (accidental or intentional), and inter-rater inconsistencies. In this paper, we explore how recent advances in machine learning may reduce manual effort and loss of information while retaining the value of human intelligence in the coding process. We present ACACIA (AI Chain for Augmented Collaborative Interaction Analysis), an AI video data analysis application which combines a range of advances in machine perception of video material for the analysis of collaborative interaction. We evaluate ACACIA's abilities, show how far we can already get, and which challenges remain. Our contribution lies in establishing a combined machine and human analysis pipeline that may be generalized to different collaborative settings and guide future research.

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Dale, Ann, Skye Buckner-Petty, and Bradley Evanoff. "O4E.2 Efficiency of autocoding programs for converting job descriptors into standard occupational classification codes." Occupational and Environmental Medicine 76, Suppl 1 (2019): A39.2—A39. http://dx.doi.org/10.1136/oem-2019-epi.107.

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IntroductionStandard Occupational Classification (SOC) codes can link work exposure data to individual health outcomes, but manually assigning job codes is laborious. We tested two recently developed automatic coding programs.MethodsWe entered self-reported job titles and industry from two existing cohorts into two publicly available autocoding programs, the NIOSH Industry and Occupation Computerized Coding System (NIOCCS) and the Standardized Occupation Coding for Computer-assisted Epidemiological Research (SOCcer), and assessed agreement between autocodes and manual coding. We also assessed agreement of several exposure values (from the Occupational Information Network, O*NET) linked by manual SOC codes versus those linked by autocodes, in order to examine how differences in coding might affect exposure assignments in general population cohort studies.ResultsNIOCCS produced SOC codes for the majority of subjects (Cohort 1: 85%; Cohort 2: 79%). The level of detail for these codes varied slightly; 6-digit SOC codes (detailed occupations) were available for 84% and 76% of cohorts A and B respectively. Comparison to manual codes showed strong agreement at the major group level (kappa=0.8 for both cohorts) and weaker agreement at the 6-digit level (kappa ≥0.4 and 0.6). SOCcer produced 6-digit SOC codes for all subjects with good agreement at the 2-digit level (kappa ≥0.6 and 0.7) and slightly lower at the 6-digit level (kappa ≥0.3 and 0.4). Agreement for O*NET exposures was very high for most comparisons within both cohorts for both programs (many ICCs>0.8).ConclusionBoth autocoding programs can be reliable tools to aid in assigning SOC codes that represent broad industry levels, with less agreement at finer levels of job codes. Given the availability of large public datasets with job information but no other work exposure data, autocoding of jobs provides exciting opportunities for analyzing work-related health outcomes in future studies.

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Fromm, Davida, Brian MacWhinney, and Cynthia K. Thompson. "Automation of the Northwestern Narrative Language Analysis System." Journal of Speech, Language, and Hearing Research 63, no. 6 (2020): 1835–44. http://dx.doi.org/10.1044/2020_jslhr-19-00267.

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Purpose Analysis of spontaneous speech samples is important for determining patterns of language production in people with aphasia. To accomplish this, researchers and clinicians can use either hand coding or computer-automated methods. In a comparison of the two methods using the hand-coding NNLA (Northwestern Narrative Language Analysis) and automatic transcript analysis by CLAN (Computerized Language Analysis), Hsu and Thompson (2018) found good agreement for 32 of 51 linguistic variables. The comparison showed little difference between the two methods for coding most general (i.e., utterance length, rate of speech production), lexical, and morphological measures. However, the NNLA system coded grammatical measures (i.e., sentence and verb argument structure) that CLAN did not. Because of the importance of quantifying these aspects of language, the current study sought to implement a new, single, composite CLAN command for the full set of 51 NNLA codes and to evaluate its reliability for coding aphasic language samples. Method Eighteen manually coded NNLA transcripts from eight people with aphasia and 10 controls were converted into CHAT (Codes for the Human Analysis of Talk) files for compatibility with CLAN commands. Rules from the NNLA manual were translated into programmed rules for CLAN computation of lexical, morphological, utterance-level, sentence-level, and verb argument structure measures. Results The new C-NNLA (CLAN command to compute the full set of NNLA measures) program automatically computes 50 of the 51 NNLA measures and generates the results in a summary spreadsheet. The only measure it does not compute is the number of verb particles. Statistical tests revealed no significant difference between C-NNLA results and those generated by manual coding for 44 of the 50 measures. C-NNLA results were not comparable to manual coding for the six verb argument measures. Conclusion Clinicians and researchers can use the automatic C-NNLA to analyze important variables required for quantification of grammatical deficits in aphasia in a way that is fast, replicable, and accessible without extensive linguistic knowledge and training.

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Shin, Won Ae, and Haejung Lee. "The development and effects of an unplugged coding program for the with 2019 kindergarten curriculum manual." korean Jouranl of Early Childhood Education 26, no. 1 (2024): 75–100. http://dx.doi.org/10.15409/riece.2024.26.1.4.

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The purpose of this study is to develop a coding program linked to the Revised Nuri Curriculum and verify its effects on children's creative thinking, learning-related social skills, and logical-mathematical intelligence. In order to achieve this goal, the effectiveness was verified through the steps of establishing the program goal, primary development of the program and modification and supplementation after field application, pre-testing, implementation of an unplugged coding program linked to the Nuri Curriculum for 5-year-old children, and post-testing. The developed program was used to conduct the study, which was carried out for 4 months at J Kindergarten in Hwaseong, where the children of the age of the study subject belong. One of the classes was assigned as the experimental group, and the other as the control group. Collected data from pre and post treatment were statistically analyzed by using covariance(ANCOVA) with SPSS WIN 22.0 program. The result showed that the coding program developed in this research has a positive impact on creative thinking, learning-related social skills, and logical-mathematical intelligence of children. The program built in this study provided meaningful implications for applying and realizing coding education in early childhood education.

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van Atteveldt, Wouter, Jan Kleinnijenhuis, and Nel Ruigrok. "Parsing, Semantic Networks, and Political Authority Using Syntactic Analysis to Extract Semantic Relations from Dutch Newspaper Articles." Political Analysis 16, no. 4 (2008): 428–46. http://dx.doi.org/10.1093/pan/mpn006.

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Analysis of political communication is an important aspect of political research. Thematic content analysis has yielded considerable success both with manual and automatic coding, but Semantic Network Analysis has proven more difficult, both for humans and for the computer. This article presents a system for an automated Semantic Network Analysis of Dutch texts. The system automatically extracts relations between political actors based on the output of syntactic analysis of Dutch newspaper articles. Specifically, the system uses pattern matching to find source constructions and determine the semantic agent and patient of relations, and name matching and anaphora resolution to identify political actors. The performance of the system is judged by comparing the extracted relations to manual codings of the same material. Results on the level of measurement indicate acceptable performance. We also estimate performance at the levels of analysis by using a case study of media authority, resulting in good correlations between the theoretical variables derived from the automatic and manual analysis. Finally, we test a number of substantive hypotheses with regression models using the automatic and manual output, resulting in highly similar models in each case. This suggests that our method has sufficient performance to be used to answer relevant political questions in a valid way.

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Parameswaran, Uma D., Jade L. Ozawa-Kirk, and Gwen Latendresse. "To live (code) or to not: A new method for coding in qualitative research." Qualitative Social Work 19, no. 4 (2019): 630–44. http://dx.doi.org/10.1177/1473325019840394.

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Coding is an integral part of qualitative research for many scholars that use interview or focus group data. However, current practices in coding require transcription of audio/visual data prior to coding. Transcription before the coding process is an essential process for data analysis and even with meticulous detail, the nuances of nonverbal behavior found in audio and video data can be missed. In this article, we propose an alternative to coding with transcripts using a method called live coding which allows for simultaneous manual coding while listening or watching audio or video recording. We compared the method of live coding with transcript coding of text using focus group data from a perinatal telehealth group addressing depression. Based on the themes that emerged from analyzing the process, it is likely that live coding can be beneficial in preserving the voice of the participant especially used within focus group data. Live coding allowed us to see and hear the participants, an empowering process which allowed intent, context, and meaning of the words to be present in the results. Further study of live coding should include using digital tools for the analysis of qualitative data.

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Shubham, Kamble1* Monika Kherade2 Bhagyshree Bishwas3 Priya Thote4 Omkar Chavhan5 Dr. Sunil Mahajan6. "Insights into Pharmaceutical Medical Coding in Clinical Trial Processes." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 849–60. https://doi.org/10.5281/zenodo.14858141.

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The increasing complexity of medical terminology in clinical trials necessitates the use of standardized medical coding systems to ensure uniformity in data interpretation. This article provides an overview of the processes involved in medical coding within clinical data management, specifically highlighting the role of established medical dictionaries like the Medical Dictionary for Regulatory Activities (MedDRA) and the World Health Organisation Drug Dictionary Enhanced (WHO-DDE). These dictionaries aid in categorizing medical terms from reports generated throughout the lifecycle of clinical trials, addressing challenges such as variations in data presentation due to diverse investigator backgrounds.  Medical coding typically involves a precoding process, followed by auto coding where terms matching those in the dictionary are automatically assigned codes. However, discrepancies may arise, necessitating manual coding to address unclear or ambiguous terms. The medical coding team plays a critical role in ensuring accurate coding by collaborating with investigators to clarify ambiguous terms and rectify errors in data recorded on Case Report Forms (CRFs).  The MedDRA structure includes multiple hierarchical levels from Low Level Terms (LLTs) to System Organ Classes (SOCs), providing a comprehensive framework for handling diverse medical terms. Common issues faced by coders, including illegible terms, abbreviations, and improper categorization, are also discussed. By understanding these processes and challenges, medical coders can enhance the reliability of clinical trial data, ultimately contributing to safer and more effective therapeutic outcomes.

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Moore, G. William, and Jules J. Berman. "Performance Analysis of Manual and Automated Systemized Nomenclature of Medicine (SNOMED) Coding." American Journal of Clinical Pathology 101, no. 3 (1994): 253–56. http://dx.doi.org/10.1093/ajcp/101.3.253.

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Koeman, T., N. Offermans, Y. Christopher, et al. "Manual coding and automatic recoding of occupational codes: consequences for exposure assessment." Occupational and Environmental Medicine 68, Suppl_1 (2011): A78. http://dx.doi.org/10.1136/oemed-2011-100382.254.

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Prathyusha, Kosuru. "Comprehensive Code Review Guidelines for Java: Integrating Best Practices with Static Code Analysis Tools." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 4 (2020): 1–7. https://doi.org/10.5281/zenodo.14183806.

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A Java code review checklist for manual and automated code reviews with best practices comprises the checklists for using manual code reviews in conjunction with static analysis tools to achieve high-quality, maintainable, and secure code products. It overtures a composite strategy that uses human intelligence together with mechanical intelligence. Checkers like SonarQube, FindBugs, and Programming Mistake Detector will help detect coding standards violations and potential bugs and security holes for tools that run on Java sources. However, code reviews are certainly not limited to these automated mechanisms, as they also use the insight of a programmer to judge the structure and readability of the code, in addition to other business aspects. The guidelines include the structure of code, control of errors, possible optimizations, and comments. They also mention the proper usage of coding style, language features of Java language, and pattern design concept. This paper shows that combining selected manual review techniques with static analysis tools helps development teams build an effective code review practice that helps improve code quality, decrease technical debt, and increase software reliability (Asaduzzaman et al., 2016).

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Hoey, Jesse, Tobias Schröder, Jonathan Morgan, Kimberly B. Rogers, Deepak Rishi, and Meiyappan Nagappan. "Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale." Small Group Research 49, no. 6 (2018): 647–83. http://dx.doi.org/10.1177/1046496418802362.

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Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.

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Sarfraz, Maheen, Syed Shakil ur Rehman, Mehwish Ikram, and Maryam Ikram. "Clinical Leadership Perspectives: Advancements in Manual Physical Therapy Practice with the Pakistani Context." International Health Review 3, no. 1 (2023): 59–72. http://dx.doi.org/10.32350/ihr.31.05.

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Physical therapy is a healthcare discipline that is dedicated to help individuals and patients in maintaining their health while optimizing their strength, function, and movements. Pakistan being a developing country, experiences a slower pace in the establishment of advancements in the medical professions. Whereas physical therapy is significantly improving day by day in Pakistan; additionally, this study aims to discuss the advances, in manual therapy concerning the physical therapy profession in Pakistan. This study aimed to evaluate the perception of clinical leadership and its recent advancements concerning the manual physical therapy practice in Pakistan for the preceding year. For this purpose, semi-structured interviews were conducted with 10 physical therapists from 5 different cities in Pakistan. Physical therapists with at least 10 years of clinical practice were included in the data collection. The interviews were audio/video recorded, data verbatim transcribed, and analyzed manually by open coding and in-vivo coding. Furthermore, thematic analysis was done to identify different elements of recent advancements concerning manual physical therapy by finding the appropriate patterns in the gathered data. The results showed that advancements occurred in the last decade, such as international certifications, workshops, hands-on practice and master programs. There is lack of awareness, gender barriers, and financial constraints in adopting new advancements. The focus was shifted more toward muscle energy technique and myofascial release. Modalities were preferred in the past for pain relief; however, in the recent decade, they have been replaced by manual therapy techniques. Pain management and prognosis are better with manual therapy. These advancements will be cost-effective for the patient. This study concluded that manual therapy in Pakistan has undergone a significant transformation during the last preceding years. There is still great potential for more advancement in manual therapy techniques in Pakistan.

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Hsu, Chien-Ju, and Cynthia K. Thompson. "Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis." Journal of Speech, Language, and Hearing Research 61, no. 2 (2018): 373–85. http://dx.doi.org/10.1044/2017_jslhr-l-17-0185.

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Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia (a) for reliability purposes to ascertain whether they yield similar results and (b) to evaluate CLAN for its ability to automatically identify language variables important for detailing agrammatic production patterns. Method The same set of Cinderella narrative samples from 8 participants with a clinical diagnosis of agrammatic aphasia and 10 cognitively healthy control participants were transcribed and coded using NNLA and CLAN. Both coding systems were utilized to quantify and characterize speech production patterns across several microsyntactic levels: utterance, sentence, lexical, morphological, and verb argument structure levels. Agreement between the 2 coding systems was computed for variables coded by both. Results Comparison of the 2 systems revealed high agreement for most, but not all, lexical-level and morphological-level variables. However, NNLA elucidated utterance-level, sentence-level, and verb argument structure–level impairments, important for assessment and treatment of agrammatism, which are not automatically coded by CLAN. Conclusions CLAN automatically and reliably codes most lexical and morphological variables but does not automatically quantify variables important for detailing production deficits in agrammatic aphasia, although conventions for manually coding some of these variables in Codes for the Human Analysis of Transcripts are possible. Suggestions for combining automated programs and manual coding to capture these variables or revising CLAN to automate coding of these variables are discussed.

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Journal articles on the topic 'Manual Coding'

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