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A Meta-Analysis of the Psychosocial, Behavioral, and Health Correlates of
Perceived Work Ability
Aaron Greenfield1,2,3
, Donald Truxillo2
Ph.D., Grant Brady2
, Cosimo Gonnelli2
, David
Caughlin2
Ph.D.
1
Oregon Institute of Occupational Health Sciences, Oregon Health and Science University, Portland OR, 2
Department of
Psychology, Portland State University, Portland OR, 3
University of Oregon, Eugene OR
Results
References
Discussion
Method
Introduction
• Over 20% of the United States population is projected to be 65 or older by 2030, compared with 9.8% in 1970 and 13% in 2010
(Colby & Ortman, 2014).
• The workforce is aging: Workers aged 55 and older are estimated to make up 25% of the US workforce by 2020 (Stanford
Center on Longevity, 2013).
• This aging workforce has created a challenge for employers and governments attempting to counteract the declines in function
that are associated with aging, such as weakening of physical strength (Metter et al., 1997) and declines in certain types of
cognitive skills such as processing speed (Bugg et al., 2006).
• In order to understand the effects of these declines on work, in the early 1980’s the Finnish Institute of Occupational Health
(Ilmarinen, 1991) developed the concept of work ability: A person’s subjective assessment that he/she can meet the physical
and psychological demands of his/her work (Hasselhorn, 2008).
• The most common instrument for measuring work ability is the Work Ability Index (WAI; Hasselhorn, 2008).
• A number of workplace variables are related to work ability: satisfaction, job control and health have shown to have a positive
relationship with work ability, while age and vocational strain have shown negative relationships (McGonagle et al., 2014;
Palermo et al., 2013; Weigl et al., 2013). However, the work ability literature has not been summarized meta-analytically.
• Purpose: In this study, we quantitatively summarize the work ability literature to identify 1) correlates of work ability and 2)
gaps in the literature.
• Hypotheses: We hypothesize that work ability will be positively correlated with vigor/vitality (H1) and self-efficacy/esteem
(H2), and negatively correlated with age (H3), body mass index (BMI; H4), burnout/fatigue (H5), and sick leave/absence (H6).
We also pose a research question with regard to gender: Will work ability be higher for males or females?
Key Findings
•Substantial relationship found between work ability and vitality, a
critical mental resource for maintaining stamina and decreasing
psychological distress (Ryan & Frederick, 1997).
•Work ability also related to key factors such as self-efficacy and
burnout/fatigue.
•Work ability related to actual sick leave absence at work.
•Negative relationship found between work ability and BMI. In other
words, perceptions of work ability are related to a measure of
objective health.
Research Gaps Uncovered
•More research is needed on other correlates of work ability such as
job satisfaction, job performance, and actual work behaviors.
Future Research
•Examination of moderators of the effects of work ability such as job
type, the measure of work ability used, and non-published studies.
•Job type: A wide variety of professions were examined in this meta-
analysis (e.g., firefighters, teachers, nurses, municipal employees, and
business managers). It would be beneficial to examine the differences
in correlates between jobs heavy in physical labor, mental labor, and
mixtures of the two.
•Workplace interventions to increase work ability need further study.
(Truxillo, Cadiz, & Hammer, 2015).
Limitations
•Only included Pearson correlations in the analysis.
•No unpublished or in-press articles.
•Correcting for these limitations may result in additional findings.
• A Boolean search using the OR operator was performed using the PsycNET Search function in order to generate a list of 906 articles
which contained at least one of the following phrases: “WAI”, “Ability to Work”, “Work Capability”, “Capacity to Work”, “Work-
Ability”, “Work Ability Index”, or “Work Ability Assessment”.
• The list was then narrowed down to 605 articles after removing those prior to1980 (the Work Ability Index was established in the
1980’s).
• The articles were then screened for having at least 1 usable effect size between work ability and a correlate, which generated a list of 202
articles.
• Only articles containing at least 1 correlation between work ability and a correlate of interest were included in the analysis, which
resulted in a list of 51 articles (see model for complete selection process: k = 59).
• The correlates of work ability were grouped into 4 broad categories by the research team.
• Demographic (age; gender)
• Psychosocial (self-efficacy/esteem, or confidence to perform a task; vigor/vitality, or perceived vitality in daily life)
• Physical Health (BMI)
• Work Outcomes (burnout/fatigue at work; sick leave/absence)
• Certain correlates (e.g., job satisfaction; retirement behaviors) were not included if there were too few studies (k<3).
• Each article was coded by two separate raters (3 of the authors) and then compared in order to ensure
inter-rater reliability.
• Once coded, a “Bare-Bones” analyses was performed in order to compute
the corrected correlations (the sum of each correlation
multiplied by the number of people in that study and then
divided by the total number of studies) of the relationships.
See equation and example to the right:
Original 906 Articles
• Bugg, J. M., Zook, N. A., DeLosh, E. L., Davalos, D. B., & Davis, H. P. (2006). Age differences in fluid intelligence: contributions of general slowing and frontal
decline. Brain and cognition, 62, 9-16.
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
• Colby, S. L., & Ortman, J. M. (2014). The baby boom cohort in the United States: 2012 to 2060. Current Population Reports. US Census Bureau, Washington,
DC: P25-1141.
• Goetzel, R. Z., Gibson, T. B., Short, M. E., Chu, B. C., Waddell, J., Bowen, J., DeJoy, D. M. (2010). A multi-worksite analysis of the relationships among body mass
index, medical utilization, and worker productivity. Journal of occupational and environmental medicine/American College of Occupational and Environmental
Medicine, 52, S52-58.
• Hasselhorn, H. M. (2008). Work ability-concept and assessment. Germany: University of Wuppertal.
• Ilmarinen, J., Tuomi, K., Eskelinen, L., Nygård, C. H., Huuhtanen, P., & Klockars, M. (1991). Summary and recommendations of a project involving cross-sectional
and follow-up studies on the aging worker in Finnish municipal occupations (1981—1985). Scandinavian journal of work, environment & health, 17, 135-141.
• McGonagle, A. K., Fisher, G. G., Barnes-Farrell, J. L., & Grosch, J. W. (2014). Individual and work factors related to perceived work ability and labor force
outcomes. Journal of Applied Psychology, 100, 376-398.
• Metter, E. J., Conwit, R., Tobin, J., & Fozard, J. L. (1997). Age-associated loss of power and strength in the upper extremities in women and men. The Journals of
Gerontology Series A: Biological Sciences and Medical Sciences, 52, B267-B276.
• Palermo, J., Fuller-Tyszkiewicz, M., Walker, A., & Appannah, A. (2013). Primary-and secondary-level organizational predictors of work ability. Journal of
occupational health psychology, 18, 220.
• Ryan, R. M., & Frederick, C. (1997). On energy, personality, and health: Subjective vitality as a dynamic reflection of well being.‐ Journal of personality,65, 529-
565.
• Stanford Center on Longevity (2013). The aging US workforce: A chartbook of demographic shifts. Stanford, CA: Hayutin, A., Beals, M., & Borges, E.
• Truxillo, D. M., Cadiz, D. E., & Hammer, L. B. (2015). Supporting the aging workforce: A review and recommendations for workplace intervention research.
Annual Review of Organizational Psychology and Organizational Behavior, 2, 351-381.
• Weigl, M., Mueller, A., Hornung, S., Zacher, H., & Angerer, P. (2013). The moderating effects of job control and selection, optimization, and compensation
strategies on the age–work ability relationship. Journal of Organizational Behavior, 34, 607-628.
Process
301 Discarded
(Published before 1980)
605 Published after
1980
202 Containing
Usable Effect Sizes
Final 51 Correlations
403 Discarded
(No Usable Effect Sizes)
151 Discarded
(No Correlations)
K=27
N=16,683
K=4
N=736
K=7
N=6,697
K=5
N=4,849
K=5
N=863
K=6
N=3,897
K=5
N=3,670
Work Ability
Self-Efficacy/Esteem
Age
BMI
Vigor/Vitality
Burnout/Fatigue
Sick Leave/Absence
Demographic
Gender
Psychosocial
Work OutcomesHealth

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Poster Template Final Version

  • 1. A Meta-Analysis of the Psychosocial, Behavioral, and Health Correlates of Perceived Work Ability Aaron Greenfield1,2,3 , Donald Truxillo2 Ph.D., Grant Brady2 , Cosimo Gonnelli2 , David Caughlin2 Ph.D. 1 Oregon Institute of Occupational Health Sciences, Oregon Health and Science University, Portland OR, 2 Department of Psychology, Portland State University, Portland OR, 3 University of Oregon, Eugene OR Results References Discussion Method Introduction • Over 20% of the United States population is projected to be 65 or older by 2030, compared with 9.8% in 1970 and 13% in 2010 (Colby & Ortman, 2014). • The workforce is aging: Workers aged 55 and older are estimated to make up 25% of the US workforce by 2020 (Stanford Center on Longevity, 2013). • This aging workforce has created a challenge for employers and governments attempting to counteract the declines in function that are associated with aging, such as weakening of physical strength (Metter et al., 1997) and declines in certain types of cognitive skills such as processing speed (Bugg et al., 2006). • In order to understand the effects of these declines on work, in the early 1980’s the Finnish Institute of Occupational Health (Ilmarinen, 1991) developed the concept of work ability: A person’s subjective assessment that he/she can meet the physical and psychological demands of his/her work (Hasselhorn, 2008). • The most common instrument for measuring work ability is the Work Ability Index (WAI; Hasselhorn, 2008). • A number of workplace variables are related to work ability: satisfaction, job control and health have shown to have a positive relationship with work ability, while age and vocational strain have shown negative relationships (McGonagle et al., 2014; Palermo et al., 2013; Weigl et al., 2013). However, the work ability literature has not been summarized meta-analytically. • Purpose: In this study, we quantitatively summarize the work ability literature to identify 1) correlates of work ability and 2) gaps in the literature. • Hypotheses: We hypothesize that work ability will be positively correlated with vigor/vitality (H1) and self-efficacy/esteem (H2), and negatively correlated with age (H3), body mass index (BMI; H4), burnout/fatigue (H5), and sick leave/absence (H6). We also pose a research question with regard to gender: Will work ability be higher for males or females? Key Findings •Substantial relationship found between work ability and vitality, a critical mental resource for maintaining stamina and decreasing psychological distress (Ryan & Frederick, 1997). •Work ability also related to key factors such as self-efficacy and burnout/fatigue. •Work ability related to actual sick leave absence at work. •Negative relationship found between work ability and BMI. In other words, perceptions of work ability are related to a measure of objective health. Research Gaps Uncovered •More research is needed on other correlates of work ability such as job satisfaction, job performance, and actual work behaviors. Future Research •Examination of moderators of the effects of work ability such as job type, the measure of work ability used, and non-published studies. •Job type: A wide variety of professions were examined in this meta- analysis (e.g., firefighters, teachers, nurses, municipal employees, and business managers). It would be beneficial to examine the differences in correlates between jobs heavy in physical labor, mental labor, and mixtures of the two. •Workplace interventions to increase work ability need further study. (Truxillo, Cadiz, & Hammer, 2015). Limitations •Only included Pearson correlations in the analysis. •No unpublished or in-press articles. •Correcting for these limitations may result in additional findings. • A Boolean search using the OR operator was performed using the PsycNET Search function in order to generate a list of 906 articles which contained at least one of the following phrases: “WAI”, “Ability to Work”, “Work Capability”, “Capacity to Work”, “Work- Ability”, “Work Ability Index”, or “Work Ability Assessment”. • The list was then narrowed down to 605 articles after removing those prior to1980 (the Work Ability Index was established in the 1980’s). • The articles were then screened for having at least 1 usable effect size between work ability and a correlate, which generated a list of 202 articles. • Only articles containing at least 1 correlation between work ability and a correlate of interest were included in the analysis, which resulted in a list of 51 articles (see model for complete selection process: k = 59). • The correlates of work ability were grouped into 4 broad categories by the research team. • Demographic (age; gender) • Psychosocial (self-efficacy/esteem, or confidence to perform a task; vigor/vitality, or perceived vitality in daily life) • Physical Health (BMI) • Work Outcomes (burnout/fatigue at work; sick leave/absence) • Certain correlates (e.g., job satisfaction; retirement behaviors) were not included if there were too few studies (k<3). • Each article was coded by two separate raters (3 of the authors) and then compared in order to ensure inter-rater reliability. • Once coded, a “Bare-Bones” analyses was performed in order to compute the corrected correlations (the sum of each correlation multiplied by the number of people in that study and then divided by the total number of studies) of the relationships. See equation and example to the right: Original 906 Articles • Bugg, J. M., Zook, N. A., DeLosh, E. L., Davalos, D. B., & Davis, H. P. (2006). Age differences in fluid intelligence: contributions of general slowing and frontal decline. Brain and cognition, 62, 9-16. • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. • Colby, S. L., & Ortman, J. M. (2014). The baby boom cohort in the United States: 2012 to 2060. Current Population Reports. US Census Bureau, Washington, DC: P25-1141. • Goetzel, R. Z., Gibson, T. B., Short, M. E., Chu, B. C., Waddell, J., Bowen, J., DeJoy, D. M. (2010). A multi-worksite analysis of the relationships among body mass index, medical utilization, and worker productivity. Journal of occupational and environmental medicine/American College of Occupational and Environmental Medicine, 52, S52-58. • Hasselhorn, H. M. (2008). Work ability-concept and assessment. Germany: University of Wuppertal. • Ilmarinen, J., Tuomi, K., Eskelinen, L., Nygård, C. H., Huuhtanen, P., & Klockars, M. (1991). Summary and recommendations of a project involving cross-sectional and follow-up studies on the aging worker in Finnish municipal occupations (1981—1985). Scandinavian journal of work, environment & health, 17, 135-141. • McGonagle, A. K., Fisher, G. G., Barnes-Farrell, J. L., & Grosch, J. W. (2014). Individual and work factors related to perceived work ability and labor force outcomes. Journal of Applied Psychology, 100, 376-398. • Metter, E. J., Conwit, R., Tobin, J., & Fozard, J. L. (1997). Age-associated loss of power and strength in the upper extremities in women and men. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 52, B267-B276. • Palermo, J., Fuller-Tyszkiewicz, M., Walker, A., & Appannah, A. (2013). Primary-and secondary-level organizational predictors of work ability. Journal of occupational health psychology, 18, 220. • Ryan, R. M., & Frederick, C. (1997). On energy, personality, and health: Subjective vitality as a dynamic reflection of well being.‐ Journal of personality,65, 529- 565. • Stanford Center on Longevity (2013). The aging US workforce: A chartbook of demographic shifts. Stanford, CA: Hayutin, A., Beals, M., & Borges, E. • Truxillo, D. M., Cadiz, D. E., & Hammer, L. B. (2015). Supporting the aging workforce: A review and recommendations for workplace intervention research. Annual Review of Organizational Psychology and Organizational Behavior, 2, 351-381. • Weigl, M., Mueller, A., Hornung, S., Zacher, H., & Angerer, P. (2013). The moderating effects of job control and selection, optimization, and compensation strategies on the age–work ability relationship. Journal of Organizational Behavior, 34, 607-628. Process 301 Discarded (Published before 1980) 605 Published after 1980 202 Containing Usable Effect Sizes Final 51 Correlations 403 Discarded (No Usable Effect Sizes) 151 Discarded (No Correlations) K=27 N=16,683 K=4 N=736 K=7 N=6,697 K=5 N=4,849 K=5 N=863 K=6 N=3,897 K=5 N=3,670 Work Ability Self-Efficacy/Esteem Age BMI Vigor/Vitality Burnout/Fatigue Sick Leave/Absence Demographic Gender Psychosocial Work OutcomesHealth