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Books on the topic 'Predictor factors'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Kosior, Christine A. Visual factors as predictors of achievement and behaviour. Sudbury, Ont: Laurentian University, Department of Psychology, 1999.

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2

Malchose, Donald Clarence. Identifying factors that predict teen driver crashes. Fargo, N.D.]: Mountain-Plains Consortium, 2011.

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3

Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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4

Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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5

Turner, Chandra Ramphal. Factors that put students at risk of leaving school before graduation. Scarborough, Ont: Program Dept., Research Centre, Scarborough Board of Education, 1993.

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6

Blanc, Jean-Luc Le. Climate information and prediction services for fisheries. Geneva, Switzerland: World Meteorological Organization, 1995.

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7

Caldwell, Mary Margaret. Factors to predict the establishment of Ontario hospital foundations. Ottawa: National Library of Canada, 1990.

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8

An introduction to risk prediction and preventive dentistry. Chicago, Ill: Quintessence Pub. Co., 1999.

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9

Perinatal risk and infant development: Assessment and prediction. New York: Guilford Press, 1989.

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10

McTavish, Donald J. Prediction and measurement of modal damping factors for viscoelastic space structures. Washington, D. C: AIAA, 1992.

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11

The future factor: The five forces transforming our lives and shaping human destiny. New York: McGraw-Hill, 2000.

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12

Snape, Melanie C. Factors that predict the psychological consequences of experiencing a traumatic event. Birmingham: University of Birmingham, 1996.

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13

Snape, Melanie C. [Factors that predict the psychological consequences of experiencing a traumatic event]. Birmingham: University of Birmingham, 1996.

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14

Loeber, Rolf. Young Homicide Offenders and Victims: Risk Factors, Prediction, and Prevention from Childhood. Boston, MA: Springer Science+Business Media, LLC, 2011.

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15

Woolley, Adam. A guide to practical toxicology: Evaluation, prediction, and risk. 2nd ed. New York: Informa Healthcare USA, 2008.

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16

A guide to practical toxicology: Evaluation, prediction and risk. London: Taylor & Francis, 2003.

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17

A guide to practical toxicology: Evaluation, prediction, and risk. 2nd ed. New York: Informa Healthcare USA, 2008.

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18

Lof, C. J. Useful lifetime prediction software for plane semi-cracks in 3-D solid configurations. Amsterdam: National Aerospace Laboratory, 1986.

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19

Diagnosis and risk prediction of dental caries. Chicago: Quintessence Pub. Co., 2000.

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20

Yi, Chi-jŏn. Noin chasal yechʻŭk mohyŏng kaebal mit yebang taechʻaek yŏnʼgu =: Study on developing elderly suicide prediction model and prevention strategy. [Seoul]: Hanʼguk Pogŏn Pokchi Illyŏk Kaebarwŏn, 2006.

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21

Pelavin, Sol H. Changing the odds: Factors increasing access to college. New York: College Entrance Examination Board, 1990.

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22

Alfred, Buch. Improvement of fatigue life prediction accuracy for various realistic loading spectra by use of correction factors. Haifa, Israel: Technion-Israel Institute of Technology, Dept. of Aeronautical Engineering, 1985.

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23

Zheng, Fenli. Shui shi guo cheng yu yu bao mo xing =: Water erosion process and prediction model. Beijing: Ke xue chu ban she, 2008.

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24

Muscular endurance in women through adulthood: A predictor of muscular strength? 1994.

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25

Vlachopoulos, Charalambos, and Nikolaos Ioakeimidis. Erectile dysfunction as a marker and predictor of cardiovascular disease. Edited by Charalambos Vlachopoulos. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0245.

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Erectile dysfunction (ED) is defined as the inability to obtain or maintain a penile erection to support satisfactory sexual performance. It is considered an early manifestation of generalized vascular disease and recognized as a marker of increased cardiovascular risk both acutely and chronically by predicting all-cause mortality, cardiovascular mortality, coronary events, stroke, and peripheral artery disease in men with and without known coronary artery disease. The link between ED and cardiovascular disease might reside in the interaction between androgen level, chronic inflammation, and cardiovascular risk factors that determine endothelial dysfunction and atherosclerosis both in the penile and coronary circulation. Because penile artery size is smaller compared with coronary arteries, the same degree of endothelial dysfunction and atherosclerotic burden causes a more significant reduction of blood flow in erectile tissues compared with that in coronary circulation. From a clinical standpoint, because ED may precede cardiovascular disease, it can be used as an early marker to identify men at higher risk of cardiovascular events. The average 3-year time period between the onset of ED symptoms and a cardiovascular event offers the opportunity for detailed cardiological assessment and intensive treatment of risk factors.

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26

Philip, Pierre, Stephanie Bioulac, Patricia Sagaspe, and Jean-Arthur Micoulaud-Franchi. Drowsy driving. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198778240.003.0021.

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Drowsy driving increases risk of traffic accidents. A major problem remains in the identification of drowsy drivers at risk for traffic accidents. Drowsy driving is the consequence of various behavioural factors (e.g. sleep duration, work duration, shift-work schedules) combined or not with sleep and iatrogenic disorders (e.g. obstructive sleep apnoea syndrome, hypersomnia, drug-induced sleepiness). Severity of sleep disorders is a non-linear predictor of traffic accident risk. In comparison, sleepiness at the wheel (SAW) can be considered as a reliable indicator of a combination of behavioural and sleep disorder factors, and is a better risk predictor. It remains thus very important to question patients about SAW when clinicians have to determine the medical fitness to drive of such patients. Because of the potential risk of under-reporting of SAW, especially in professional drivers, objective measures can help to complement the clinical evaluation. Further researches are needed to optimize objective measures able to predict the risk of traffic accidents due to drowsy driving.

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27

Hechtman, Lily, ed. Attention Deficit Hyperactivity Disorder. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190213589.001.0001.

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The book provides a comprehensive summary of the best known and most highly respected well-controlled long-term prospective follow-up studies in Attention Deficit Hyperactivity Disorder (ADHD). These studies followed children with ADHD and matched controls into young adulthood (mean age 20–25 years) and middle age (mean age 41 years). They explore a wide variety of clinically relevant outcome areas, such as education, occupation, emotional and psychiatric functioning, substance use and abuse, sexual behavior, and legal problems. One chapter focuses particularly on the outcome of girls with ADHD. The book also explores possible predictors of adult outcome. A whole chapter is devoted to treatment (medication and psychosocial) as a predictor of outcome. In addition to treatment, predictors explored include characteristics of the child (e.g., IQ, severity of initial ADHD symptoms, initial comorbidity) and characteristics of the family (e.g., socioeconomic status, single parenthood, parental pathology, and family functioning). A summary chapter explores the impact and importance of these predictors in various outcome areas, such as education, occupation, emotional/social functioning, antisocial behavior, substance use and abuse, and risky sexual and driving behaviors. Professionals and the general public will come away with a clear view of what can happen to children with ADHD as they proceed through adolescence and adulthood. The book also addresses important prognostic and predictive factors in treatment approaches to ensure better long-term outcome in patients with ADHD.

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28

W, Maris Ronald, and American Association of Suicidology, eds. Assessment and Prediction of Suicide. New York: Guilford Press, 1992.

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29

Chiu, Chi-yue, and Letty Y. Y. Kwan. National and Historical Variations in Innovation Performance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190455675.003.0004.

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Innovation performance differs across nations. Within the same nation, innovation performance also varies across time. In the first part of this chapter, we discuss some cultural, political, and institutional factors that explain historical variations in a country’s innovation performance. In the second part of this chapter, using the multinational data from the Global Innovation Index (GII), we focus on the extent to which people in a country rely on financial institutions to mitigate risks in daily life as a proximal predictor of innovation performance at the national level. We argue that the tendency to rely on formal institutions to mitigate risks in everyday life reflects the level of institutional trust, which is a factor that contributes to both cross-national and historical variations in innovation performance. We further argue that institutional trust plays a particularly important role in promoting innovation in countries that lack strong institutional support for innovation.

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30

Jacobi, Corinna, Kristian Hütter, and Eike Fittig. Psychosocial Risk Factors for Eating Disorders. Edited by W. Stewart Agras and Athena Robinson. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190620998.013.6.

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This chapter provides an updated overview of risk factors for eating disorders, on the basis of the risk factor taxonomy described by (Kraemer et al., 1997). It summarizes risk factors identified in longitudinal studies and markers and retrospective correlates from cross-sectional studies through April 2002 for anorexia nervosa, bulimia nervosa, and binge eating disorder, identifies new studies published between May 2002 and June 2015, and integrates them into the earlier review. The updated review confirms that longitudinal evidence on risk factors is strongest for nonspecific eating disorder diagnoses including subclinical forms and weakest for participants with diagnoses of anorexia nervosa. When strict criteria for caseness are applied, the majority of risk factors were not able to predict distinct diagnoses and only very few risk factors were confirmed in more than one sample. Case prediction, specificity, and replication therefore remain the biggest challenges in risk factor research for eating disorders.

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31

Rick, Archer, and U.S. Army Research Institute for the Behavioral and Social Sciences., eds. Improving soldier factors in prediction models. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2002.

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32

van Dulmen, Manfred H. M., and Haylee DeLuca. Former Foster Care Youth and Resilient Functioning in Young Adulthood. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190260637.003.0043.

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This chapter focuses on former foster care youth and resilient functioning in young adulthood, particularly predictors of success and future directions. The chapter reviews the literature on predictors of (a) work and educational competence, (b) competence in close relationships, and (c) self-competence. The chapter reviews empirical evidence for success components related to pre-placement, placement, individual, and contextual factors. The chapter concludes with five recommendations for future research: (1) strength-based developmental assessments of competence and resilience, (2) identification of factors that differentiate predicting specific areas of competence/resiliency, (3) continued theoretically informed work that is guided by developmental science, (4) studies guided by person-centered frameworks and analyses, and (5) studies that identify modifiable placement factors that predict successful outcomes among former foster care youth.

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33

Bashor, Terry W. Contributions of non-intellective factors to the prediction of academic performance of law students admitted with average intellective predictors. 1987.

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34

Zey, Michael G. Future Factor: Forces Transforming Human Destiny. Taylor & Francis Group, 2017.

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35

LOK, Johnny Ch. Prediction Factors Influence Airline Fuel Price Changing Reasons. Independently published, 2018.

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36

Numbers, Prediction, and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles, 1985. Hero Books, 1985.

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37

Physiological and anthropometric factors as predictors of flatwater kayaking performance. 1990.

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38

Logan, Scott. Physiological and anthropometric factors as predictors of flatwater kayaking performance. 1991.

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39

Logan, Scott. Physiological and anthropometric factors as predictors of flatwater kayaking performance. 1991.

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40

Devon, Angela. An exploration of factors which predict client satisfaction. 1996.

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41

Discriminatory factors that identify successful basketball performers. 1986.

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42

Factors influencing adherence to positive health behaviors. 1985.

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43

Factors influencing adherence to positive health behaviors. 1988.

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44

Prediction of risk for patients with unstable angina (Evidence report/technology assessment). Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, 2000.

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45

The Future Factor: Forces Transforming Human Destiny. Transaction Publishers, 2004.

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46

Axelsson, Per. An Introduction to Risk Prediction and Preventive Dentistry. Quintessence Publishing (IL), 1999.

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47

Factors affecting academic achievement of collegiate athletes. 1990.

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48

Factors affecting academic achievement of collegiate athletes. 1991.

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49

Inglis, Gordon Walter. Discriminatory factors that identify successful basketball performers. 1986.

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50

Discriminatory factors that identify successful basketball performers. 1986.

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