Relevant bibliographies by topics / Language samples

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Language samples'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Language samples"

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Barokova, Mihaela, and Helen Tager-Flusberg. "Commentary: Measuring Language Change Through Natural Language Samples." Journal of Autism and Developmental Disorders 50, no. 7 (June 5, 2018): 2287–306. http://dx.doi.org/10.1007/s10803-018-3628-4.

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Long, Steven H. "‘Computerized profiling’ of clinical language samples." Clinical Linguistics & Phonetics 1, no. 1 (January 1987): 97–105. http://dx.doi.org/10.1080/02699208708985005.

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Murthy, Kavi Narayana, and G. Bharadwaja Kumar. "Language identification from small text samples*." Journal of Quantitative Linguistics 13, no. 1 (January 2006): 57–80. http://dx.doi.org/10.1080/09296170500500694.

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Costanza-Smith, Amy. "The Clinical Utility of Language Samples." Perspectives on Language Learning and Education 17, no. 1 (March 2010): 9–15. http://dx.doi.org/10.1044/lle17.1.9.

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Abstract Speech-language pathologists typically use standardized assessments to diagnose language disorders. Although standardized tests are important in diagnosing school-age language disorders, the use of language sample analysis should not be ignored. This article summarizes the benefits of language sample analysis and introduces considerations for collecting and analyzing language samples.
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Eisenberg, Sarita L., and Ling-Yu Guo. "Sample Size for Measuring Grammaticality in Preschool Children From Picture-Elicited Language Samples." Language, Speech, and Hearing Services in Schools 46, no. 2 (April 2015): 81–93. http://dx.doi.org/10.1044/2015_lshss-14-0049.

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Purpose The purpose of this study was to investigate whether a shorter language sample elicited with fewer pictures (i.e., 7) would yield a percent grammatical utterances (PGU) score similar to that computed from a longer language sample elicited with 15 pictures for 3-year-old children. Method Language samples were elicited by asking forty 3-year-old children with varying language skills to talk about pictures in response to prompts. PGU scores were computed for each of two 7-picture sets and for the full set of 15 pictures. Results PGU scores for the two 7-picture sets did not differ significantly from, and were highly correlated with, PGU scores for the full set and with each other. Agreement for making pass–fail decisions between each 7-picture set and the full set and between the two 7-picture sets ranged from 80% to 100%. Conclusion The current study suggests that the PGU measure is robust enough that it can be computed on the basis of 7, at least in 3-year-old children whose language samples were elicited using similar procedures.
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Channell, Ron W., and Bonnie W. Johnson. "Automated Grammatical Tagging of Child Language Samples." Journal of Speech, Language, and Hearing Research 42, no. 3 (June 1999): 727–34. http://dx.doi.org/10.1044/jslhr.4203.727.

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Recent studies of the automated grammatical categorization ("tagging") of words using probabilistic methods have reported substantial levels of accuracy—over 95% agreement with manual tagging for words from a variety of texts. However, the texts with which this method has been tested were written by adults and edited by publishers. The present study examined the accuracy with which such methods could tag transcribed conversational language samples from 30 normally developing children. On a word-by-word basis, automated accuracy levels ranged from 92.9% to 97.4%, averaging 95.1%. Accuracy at correctly tagging whole utterances was lower, ranging from 60.5% to 90.3%, with an average of 77.7%. Probabilistic methods of coding language samples hold potential as a viable tool for child language research. Further study and improvement of automated grammatical tagging is warranted and necessary before widespread use can be made of this technology.
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Nelson, Nickola Wolf, and Adelia M. Van Meter. "Measuring Written Language Ability in Narrative Samples." Reading & Writing Quarterly 23, no. 3 (April 25, 2007): 287–309. http://dx.doi.org/10.1080/10573560701277807.

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Rijkhoff, Jan, Dik Bakker, Kees Hengeveld, and Peter Kahrel. "A Method of Language Sampling." Studies in Language 17, no. 1 (January 1, 1993): 169–203. http://dx.doi.org/10.1075/sl.17.1.07rij.

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In recent years more attention is being paid to the quality of language samples in typological work. Without an adequate sampling strategy, samples may suffer from various kinds of bias. In this article we propose a sampling method in which the genetic criterion is taken as the most important: samples created with this method will reflect optimally the diversity of the languages of the world. On the basis of the internal structure of each genetic language tree a measure is computed that reflects the linguistic diversity in the language families represented by these trees. This measure is used to determine how many languages from each phylum should be selected, given any required sample size.
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Wren, Carol T. "Collecting Language Samples from Children with Syntax Problems." Language, Speech, and Hearing Services in Schools 16, no. 2 (April 1985): 83–102. http://dx.doi.org/10.1044/0161-1461.1602.83.

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Although it is important that children produce enough spontaneous language to be analyzed, length is not the only criterion to be considered when collecting language samples. This article suggests that comparability, representativeness, and typicality must also be considered when selecting language elicitation tasks: A framework is suggested as a basis for selecting or devising tasks, and one successful battery is described which has been field tested on language-disordered children with syntax problems. These tasks elicit a wide variety of language from the children as well as stimulate them to produce a large, representative corpus of utterances.
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Tommerdahl, Jodi, and Cynthia D. Kilpatrick. "The reliability of morphological analyses in language samples." Language Testing 31, no. 1 (July 2, 2013): 3–18. http://dx.doi.org/10.1177/0265532213485570.

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Dissertations / Theses on the topic "Language samples"

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Scoville, Christine Beate. "Noun Clauses in Clinical Child Language Samples." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3545.

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Noun clauses are grammatical constructions that are of relevance both to typical language development and impaired language development. These clauses have been part of published techniques for the clinical analysis of language samples, and computer software for the automated analysis of clinical language samples has attempted to identify noun clauses, with limited success. The present study examined the development and clinical use of noun clauses as well as the automated identification of these clauses. Two sets of language samples were examined. One set consisted of 10 children with specific language impairment (SLI) whose age ranged from 7;6 to 11;1 (years;months), 10 peers matched for language development equivalence, and 10 peers matched for chronological age. The second set of samples were from 30 children considered to be typically developing, who ranged in age from 2;6 to 7;11. Language sample utterances were manually coded for the presence of noun clauses (including wh- noun clauses, that- noun clauses, and gerunds.) Samples were then automatically tagged using software. Results were tabulated and compared for accuracy. ANCOVA revealed that differences in the frequencies of WH-infinitive noun clauses and gerunds were significant between the matched groups. "Zero that clauses" (that-noun clauses containing no subordinator that) and gerunds were significantly correlated with age. Kappa levels revealed that agreement between manual and automated coding was high on WH-infinitive clauses, gerunds, and finite wh-noun clauses.
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Millet, Deborah. "Automated grammatical tagging of language samples from children with and without language impairment /." Diss., CLICK HERE for online access:, 2001. http://contentdm.lib.byu.edu/ETD/image/etd35.pdf.

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Millet, Deborah. "Automated Grammatical Tagging of Language Samples from Children with and without Language Impairment." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/1139.

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Grammatical classification ("tagging") of words in language samples is a component of syntactic analysis for both clinical and research purposes. Previous studies have shown that probability-based software can be used to tag samples from adults and typically-developing children with high (about 95%) accuracy. The present study found that similar accuracy can be obtained in tagging samples from school-aged children with and without language impairment if the software uses tri-gram rather than bi-gram probabilities and large corpora are used to obtain probability information to train the tagging software.
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Clark, Jessica Celeste. "Automated Identification of Adverbial Clauses in Child Language Samples." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2803.pdf.

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Brown, Brittany Cheree. "Automated Identification of Adverbial Clauses in Child Language Samples." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3404.

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Adverbial clauses are grammatical constructions that are of relevance in both typical language development and impaired language development. In recent years, computer software has been used to assist in the automated analysis of clinical language samples. This software has attempted to accurately identify adverbial clauses with limited success. The present study investigated the accuracy of software for the automated identification of adverbial clauses. Two separate collections of language samples were used. One collection included 10 children with language impairment, with ages ranging from 7;6 to 11;1 (years;months), 10 age-matched peers,and 10 language-matched peers. A second collection contained 30 children ranging from 2;6 to 7;11 in age, with none considered to have language or speech impairments. Language sample utterances were manually coded for the presence of adverbial clauses (both finite and non-finite). Samples were then automatically tagged using the computer software. Results were tabulated and compared for accuracy. ANOVA revealed differences in frequencies of so-adverbial clauses whereas ANACOVA revealed differences in frequencies of both types of finite adverbial clauses. None of the structures were significantly correlated with age; however, frequencies of both types of finite adverbial clauses were correlated with mean length of utterance. Kappa levels revealed that agreement between manual and automated coding was high on both types of finite adverbial clauses.
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Michaelis, Hali Anne. "Automated Identification of Relative Clauses in Child Language Samples." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1997.

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Previously existing computer analysis programs have been unable to correctly identify many complex syntactic structures thus requiring further manual analysis by the clinician. Complex structures, including the relative clause, are of interest in child language samples due to the difference in development between children with and without language impairment. The purpose of this study was to assess the comparability of results from a new automated program, Cx, to results from manual identification of relative clauses. On language samples from 10 children with language impairment (LI), 10 language matched peers (LA), and 10 chronologically age matched peers (CA), a computerized analysis based on probabilities of sequences of grammatical markers agreed with a manual analysis with a Kappa of 0.88.
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Manning, Britney Richey. "Automated Identification of Noun Clauses in Clinical Language Samples." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2197.

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The identification of complex grammatical structures including noun clauses is of clinical importance because differences in the use of these structures have been found between individuals with and without language impairment. In recent years, computer software has been used to assist in analyzing clinical language samples. However, this software has been unable to accurately identify complex syntactic structures such as noun clauses. The present study investigated the accuracy of new software, called Cx, in identifying finite wh- and that-noun clauses. Two sets of language samples were used. One set included 10 children with language impairment, 10 age-matched peers, and 10 language-matched peers. The second set included 40 adults with mental retardation. Levels of agreement between computerized and manual analysis were similar for both sets of language samples; Kappa levels were high for wh-noun clauses and very low for that-noun clauses.
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Ehlert, Erika E. "Automated Identification of Relative Clauses in Child Language Samples." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3615.

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Relative clauses are grammatical constructions that are of relevance in both typical and impaired language development. Thus, the accurate identification of these structures in child language samples is clinically important. In recent years, computer software has been used to assist in the automated analysis of clinical language samples. However, this software has had only limited success when attempting to identify relative clauses. The present study explores the development and clinical importance of relative clauses and investigates the accuracy of the software used for automated identification of these structures. Two separate collections of language samples were used. The first collection included 10 children with language impairment, ranging in age from 7;6 to 11;1 (years;months), 10 age-matched peers, and 10 language-matched peers. A second collection contained 30 children considered to have typical speech and language skills and who ranged in age from 2;6 to 7;11. Language samples were manually coded for the presence of relative clauses (including those containing a relative pronoun, those without a relative pronoun and reduced relative clauses). These samples were then tagged using computer software and finally tabulated and compared for accuracy. ANACOVA revealed a significant difference in the frequency of relative clauses containing a relative pronoun but not for those without a relative pronoun nor for reduce relative clauses. None of the structures were significantly correlated with age; however, frequencies of both relative clauses with and without relative pronouns were correlated with mean length of utterance. Kappa levels revealed that agreement between manual and automated coding was relatively high for each relative clause type and highest for relative clauses containing relative pronouns.
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Winiecke, Rachel Christine. "Precoding and the Accuracy of Automated Analysis of Child Language Samples." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5867.

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Language sample analysis is accepted as the gold standard in child language assessment. Unfortunately it is often viewed as too time consuming for the practicing clinician. Over the last 15 years a great deal of research has been invested in the automated analysis of child language samples to make the process more time efficient. One step in the analysis process may be precoding the sample, as is used in the Systematic Analysis of Language Transcripts (SALT) software. However, a claim has been made (MacWhinney, 2008) that such precoding in fact leads to lower accuracy because of manual coding errors. No data on this issue have been published. The current research measured the accuracy of language samples analyzed with and without SALT precoding. This study also compared the accuracy of current software to an older version called GramCats (Channell & Johnson 1999). The results presented support the use of precoding schemes such as SALT and suggest that the accuracy of automated analysis has improved over time.
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Redd, Nicole. "Automated grammatical tagging of language samples from Spanish-speaking children learning English /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1276.pdf.

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Books on the topic "Language samples"

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A, Jolliffe David, ed. Academic writing: Genres, samples, and resources. New York: Pearson Longman, 2005.

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M, Kilborn Judith, and Lokke Agnes M, eds. Business writing: Strategies and samples. New York: Macmillan, 1987.

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author, Clark Jessica Wherry, ed. The legal writing companion: Problems, solutions, and samples. Durham, North Carolina: Carolina Academic Press, 2014.

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Samples of student's writing: From the English 33 diploma examinations, June 1988. [Edmonton, Alta.]: Alberta Education, Student Evaluation and Records, 1988.

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Samples of student writing from English 30 & 33 diploma examinations, January 1987. [Edmonton]: Alberta Education, Student Evaluation and Records Branch, 1987.

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Samples of students' writing from the English 33 diploma examinations January and June 1994. Edmonton, AB: Alberta Education, 1994.

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Samples of students' writing from the grade 6 English language arts achievement test, June 1988. [Edmonton, Alta.]: Alberta Education, 1988.

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Sullivan, Patrick. What is "college-level writing"?: Assignments, readings, and student writing samples. Urbana, Ill: National Council of Teachers of English, 2010.

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Education, Alberta Alberta, ed. Samples of students' writing from the grade 6 English language arts achievement test, June 1992. Edmonton, Alta: Alberta Education, 1993.

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Samples of students' writing from the grade 9 English language arts achievement test, June 1994. Edmonton, AB: Alberta Education, 1995.

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Book chapters on the topic "Language samples"

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Barokova, Mihaela. "Measuring Language Change Through Natural Language Samples." In Encyclopedia of Autism Spectrum Disorders, 1–2. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-6435-8_102486-1.

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Barokova, Mihaela. "Measuring Language Change Through Natural Language Samples." In Encyclopedia of Autism Spectrum Disorders, 2832–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_102486.

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Grieve-Smith, Angus. "Capturing Language Diversity Through Representative Samples." In Building a Representative Theater Corpus, 7–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32402-5_2.

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Wang, Xiaojie, and Yuji Matsumoto. "Improving Word Sense Disambiguation by Pseudo-samples." In Natural Language Processing – IJCNLP 2004, 386–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_41.

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Shen, Libin, and Aravind K. Joshi. "Flexible Margin Selection for Reranking with Full Pairwise Samples." In Natural Language Processing – IJCNLP 2004, 446–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_47.

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Rowland, Caroline F., Sarah L. Fletcher, and Daniel Freudenthal. "How big is big enough? Assessing the reliability of data from naturalistic samples." In Trends in Language Acquisition Research, 1–24. Amsterdam: John Benjamins Publishing Company, 2008. http://dx.doi.org/10.1075/tilar.6.04row.

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Van Lancker Sidtis, Diana. "Formulaic and novel language in a 'dual process' model of language competence: Evidence from surveys, speech samples, and schemata." In Typological Studies in Language, 445. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/tsl.83.11van.

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Long, Steven. "3 ‘Computerized Profiling’ of Clinical Language Samples and the Issue of Time." In Assessing Grammar, edited by Martin J. Ball, David Crystal, and Paul Fletcher, 29–42. Bristol, Blue Ridge Summit: Multilingual Matters, 2012. http://dx.doi.org/10.21832/9781847696397-005.

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Brandão, Silvia Figueiredo, and Silvia Rodrigues Vieira. "Chapter 11. The agreement continuum in urban samples of African, Brazilian and European varieties of Portuguese." In The Portuguese Language Continuum in Africa and Brazil, 267–89. Amsterdam: John Benjamins Publishing Company, 2018. http://dx.doi.org/10.1075/ihll.20.12bra.

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Musolff, Andreas. "The Nation as a Body or Person in Other European L1 Language Samples: Hungarian, Lithuanian and Greek." In National Conceptualisations of the Body Politic, 131–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8740-5_9.

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Conference papers on the topic "Language samples"

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Gorman, Kyle, Steven Bedrick, Geza Kiss, Eric Morley, Rosemary Ingham, Metrah Mohammed, Katina Papadakis, and Jan van Santen. "Automated morphological analysis of clinical language samples." In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/w15-1213.

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Xiong, Lin, Licheng Jiao, and Fei Yin. "Classifications with transferred samples based on RF-spaces." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009918.

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Lee, Sophia Yat Mei, Daming Dai, Shoushan Li, and Kathleen Ahrens. "Extracting Pseudo-Labeled Samples for Sentiment Classification Using Emotion Keywords." In 2011 International Conference on Asian Language Processing (IALP). IEEE, 2011. http://dx.doi.org/10.1109/ialp.2011.61.

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Remnev, N. V. "NATIVE LANGUAGE IDENTIFICATION FOR RUSSIAN USING ERRORS TYPES." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-1123-1133.

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The task of recognizing the author’s native (Native Language Identification—NLI) language based on a texts, written in a language that is non-native to the author—is the task of automatically recognizing native language (L1). The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 and 2017, where TOEFL English essays and essay samples were used as data. There is also a small number of works where the NLI problem was solved for other languages. The NLI problem was investigated for Russian by Ladygina (2017) and Remnev (2019). This paper discusses the use of well-established approaches in the NLI Shared Task 2013 and 2017 competitions to solve the problem of recognizing the author’s native language, as well as to recognize the type of speaker—learners of Russian or Heritage Russian speakers. Native language identification task is also solved based on the types of errors specific to different languages. This study is data-driven and is possible thanks to the Russian Learner Corpus developed by the Higher School of Economics (HSE) Learner Russian Research Group on the basis of which experiments are being conducted.
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Asgari, Meysam, Izhak Shafran, and Alireza Bayestehtashk. "Robust detection of voiced segments in samples of everyday conversations using unsupervised HMMS." In 2012 IEEE Spoken Language Technology Workshop (SLT 2012). IEEE, 2012. http://dx.doi.org/10.1109/slt.2012.6424264.

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Abdullaeva, Albina. "CONDUCTING LESSONS WITH SPEECH SAMPLES IN TEACHING ENGLISH LANGUAGE FOR ESP LEARNERS." In EDUCATION AND SCIENCE OF TODAY: INTERSECTORAL ISSUES AND DEVELOPMENT OF SCIENCES. European Scientific Platform, 2021. http://dx.doi.org/10.36074/logos-19.03.2021.v2.44.

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Kang, Xin, Yunong Wu, and Zhifei Zhang. "Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification." In Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-3112.

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Liang, Zujie, Weitao Jiang, Haifeng Hu, and Jiaying Zhu. "Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.265.

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Chakraborty, Rupayan, and Utpal Garain. "Role of Synthetically Generated Samples on Speech Recognition in a Resource-Scarce Language." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.400.

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Akkaralaertsest, Thaweewong, and Thaweesak Yingthawornsuk. "Classification of Depressed Speech Samples with Spectral Energy Ratios as Depression Indicator." In 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2019. http://dx.doi.org/10.1109/isai-nlp48611.2019.9045167.

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Reports on the topic "Language samples"

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Stills, Morgan. Language Sample Length Effects on Various Lexical Diversity Measures: An Analysis of Spanish Language Samples from Children. Portland State University Library, January 2016. http://dx.doi.org/10.15760/honors.250.

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Lucas, Karen. Pragmatic deficits in normal, articulation disordered, and language delayed samples. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5007.

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Navas, Soledad. A comparison of Spanish language samples elicited by the investigator in the clinic and by the mothers in the home. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2986.

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Dubeck, Margaret M., Jonathan M. B. Stern, and Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, June 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.

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The Early Grade Reading Assessment (EGRA) is used to evaluate studies and monitor projects that address reading skills in low- and middle-income countries. Results are often described solely in terms of a passage-reading subtask, thereby overlooking progress in related skills. Using archival data of cohort samples from Uganda at two time points in three languages (Ganda, Lango, and Runyankore-Rukiga), we explored a methodology that uses passage-reading results to create five learner profiles: Nonreader, Beginner, Instructional, Fluent, and Next-Level Ready. We compared learner profiles with results on other subtasks to identify the skills students would need to develop to progress from one profile to another. We then used regression models to determine whether students’ learner profiles were related to their results on the various subtasks. We found membership in four categories. We also found a shift in the distribution of learner profiles from Grade 1 to Grade 4, which is useful for establishing program effectiveness. The distribution of profiles within grades expanded as students progressed through the early elementary grades. We recommend that those who are discussing EGRA results describe students by profiles and by the numbers that shift from one profile to another over time. Doing so would help describe abilities and instructional needs and would show changes in a meaningful way.
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Hernández, Ana, Magaly Lavadenz, and JESSEA YOUNG. Mapping Writing Development in Young Bilingual Learners. CEEL, 2012. http://dx.doi.org/10.15365/ceel.article.2012.2.

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A growing interest in Two-Way Bilingual Immersion (TWBI) programs has led to increased attention to bilingualism, biliteracy, and biculturalism. This article describes the writing development in Spanish and English for 49 kindergarten students in a 50/50 Two-Way Bilingual Immersion program. Over the course of an academic year, the authors collected writing samples to analyze evidence of cross-linguistic resource sharing using a grounded theoretical approach to compare and contrast writing samples to determine patterns of cross-linguistic resource sharing in English and Spanish. The authors identified four patterns: phonological, syntactic, lexical, and metalinguistic awareness. Findings indicated that emergent writers applied similar strategies as older bilingual students, including lexical level code-switching, applied phonological rules of L1 to their respective L2s, and used experiential and content knowledge to write in their second language. These findings have instructional implications for both English Learners and native English speakers as well as for learning from students for program improvement.
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Lumpkin, Shamsie, Isaac Parrish, Austin Terrell, and Dwayne Accardo. Pain Control: Opioid vs. Nonopioid Analgesia During the Immediate Postoperative Period. University of Tennessee Health Science Center, July 2021. http://dx.doi.org/10.21007/con.dnp.2021.0008.

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Abstract:
Background Opioid analgesia has become the mainstay for acute pain management in the postoperative setting. However, the use of opioid medications comes with significant risks and side effects. Due to increasing numbers of prescriptions to those with chronic pain, opioid medications have become more expensive while becoming less effective due to the buildup of patient tolerance. The idea of opioid-free analgesic techniques has rarely been breached in many hospitals. Emerging research has shown that opioid-sparing approaches have resulted in lower reported pain scores across the board, as well as significant cost reductions to hospitals and insurance agencies. In addition to providing adequate pain relief, the predicted cost burden of an opioid-free or opioid-sparing approach is significantly less than traditional methods. Methods The following groups were considered in our inclusion criteria: those who speak the English language, all races and ethnicities, male or female, home medications, those who are at least 18 years of age and able to provide written informed consent, those undergoing inpatient or same-day surgical procedures. In addition, our scoping review includes the following exclusion criteria: those who are non-English speaking, those who are less than 18 years of age, those who are not undergoing surgical procedures while admitted, those who are unable to provide numeric pain score due to clinical status, those who are unable to provide written informed consent, and those who decline participation in the study. Data was extracted by one reviewer and verified by the remaining two group members. Extraction was divided as equally as possible among the 11 listed references. Discrepancies in data extraction were discussed between the article reviewer, project editor, and group leader. Results We identified nine primary sources addressing the use of ketamine as an alternative to opioid analgesia and post-operative pain control. Our findings indicate a positive correlation between perioperative ketamine administration and postoperative pain control. While this information provides insight on opioid-free analgesia, it also revealed the limited amount of research conducted in this area of practice. The strategies for several of the clinical trials limited ketamine administration to a small niche of patients. The included studies provided evidence for lower pain scores, reductions in opioid consumption, and better patient outcomes. Implications for Nursing Practice Based on the results of the studies’ randomized controlled trials and meta-analyses, the effects of ketamine are shown as an adequate analgesic alternative to opioids postoperatively. The cited resources showed that ketamine can be used as a sole agent, or combined effectively with reduced doses of opioids for multimodal therapy. There were noted limitations in some of the research articles. Not all of the cited studies were able to include definitive evidence of proper blinding techniques or randomization methods. Small sample sizes and the inclusion of specific patient populations identified within several of the studies can skew data in one direction or another; therefore, significant clinical results cannot be generalized to patient populations across the board.
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