Total Worker Health: A Promising Approach to a Safer and Healthier Workforce
Bibliographic record
Abstract
Editorials16 August 2016Total Worker Health: A Promising Approach to a Safer and Healthier WorkforceRobert K. McLellan, MD, MPHRobert K. McLellan, MD, MPHFrom Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M16-0965 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail More than 151 million people work in the United States. Their jobs are not merely an economic engine for our society—work serves as a key social determinant of health. With work comes income; social connection; and ideally, meaningful activity and benefits to pay for health care. However, since Hippocrates, observant physicians have also seen a plethora of adverse health effects associated with occupational hazards.The Occupational Safety and Health Administration mandates that, whenever feasible, employers use the most effective approaches to prevent harm from work. The preferred hierarchy of controlling hazards recognizes the primacy of engineering reductions in hazards, with ...References1. Sorensen G, Landsbergis P, Hammer L, Amick BC, Linnan L, Yancey A, et al; Workshop Working Group on Worksite Chronic Disease Prevention. Preventing chronic disease in the workplace: a workshop report and recommendations. Am J Public Health. 2011;101 Suppl 1:S196-207. [PMID: 21778485] doi:10.2105/AJPH.2010.300075 CrossrefMedlineGoogle Scholar2. Loeppke R, Taitel M, Haufle V, Parry T, Kessler RC, Jinnett K. Health and productivity as a business strategy: a multiemployer study. J Occup Environ Med. 2009;51:411-28. [PMID: 19339899] doi:10.1097/JOM.0b013e3181a39180 CrossrefMedlineGoogle Scholar3. Goetzel RZ, Henke RM, Tabrizi M, Pelletier KR, Loeppke R, Ballard DW, et al. Do workplace health promotion (wellness) programs work? J Occup Environ Med. 2014;56:927-34. [PMID: 25153303] doi:10.1097/JOM.0000000000000276 CrossrefMedlineGoogle Scholar4. Schulte PA, Pandalai S, Wulsin V, Chun H. Interaction of occupational and personal risk factors in workforce health and safety. Am J Public Health. 2012;102:434-48. [PMID: 22021293] doi:10.2105/AJPH.2011.300249 CrossrefMedlineGoogle Scholar5. National Institute for Occupational Safety and Health (NIOSH). National occupational research agenda (NORA): national Total Worker Health agenda (2016–2026): a national agenda to advance Total Worker Health research, practice, policy, and capacity. Cincinnati: U.S. Department of Health and Human Services (DHHS), Centers for Disease Control and Prevention, and NIOSH; 2016. DHHS (NIOSH) publication 2016–119. Accessed at www.cdc.gov/niosh/docs/2016-114/pdfs/nationaltwhagenda2016-1144-14-16.pdf on 23 April 2016. Google Scholar6. Feltner C, Peterson K, Palmieri Weber R, Cluff L, Coker-Schwimmer E, Viswanathan M, et al. The effectiveness of Total Worker Health interventions: a systematic review for a National Institutes of Health Pathways to Prevention Workshop. Ann Intern Med. 2016;165:262-9. doi:10.7326/M16-0626 LinkGoogle Scholar7. Anger WK, Elliot DL, Bodner T, Olson R, Rohlman DS, Truxillo DM, et al. Effectiveness of Total Worker Health interventions. J Occup Health Psychol. 2015;20:226-47. [PMID: 25528687] doi:10.1037/a0038340 CrossrefMedlineGoogle Scholar8. Bradley CJ, Grossman DC, Hubbard RA, Ortega AN, Curry SJ. Integrated interventions for improving Total Worker Health: a panel report from the National Institutes of Health Pathways to Prevention Workshop: Total Worker Health—What's Work Got to Do With It? Ann Intern Med. 2016;165:279-83. doi:10.7326/M16-0740 LinkGoogle Scholar9. Tomek IM, Sabel AL, Froimson MI, Muschler G, Jevsevar DS, Koenig KM, et al. A collaborative of leading health systems finds wide variations in total knee replacement delivery and takes steps to improve value. Health Aff (Millwood). 2012;31:1329-38. [PMID: 22571844] doi:10.1377/hlthaff.2011.0935 CrossrefMedlineGoogle Scholar10. Politi BJ, Arena VC, Schwerha J, Sussman N. Occupational medical history taking: how are today's physicians doing? A cross-sectional investigation of the frequency of occupational history taking by physicians in a major U.S. teaching center. J Occup Environ Med. 2004;46:550-5. [PMID: 15213517] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M16-0965.Corresponding Author: Robert K. McLellan, MD, MPH, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756; e-mail, robert.k.[email protected]org.This article was published at www.annals.org on 31 May 2016. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoIntegrated Interventions for Improving Total Worker Health: A Panel Report From the National Institutes of Health Pathways to Prevention Workshop: Total Worker Health—What's Work Got to Do With It? Cathy J. Bradley , David C. Grossman , Rebecca A. Hubbard , Alexander N. Ortega , and Susan J. Curry NIOSH Response to the NIH Pathways to Prevention Workshop Recommendations John Howard , Chia-Chia Chang , Anita L. Schill , and L. Casey Chosewood The Effectiveness of Total Worker Health Interventions: A Systematic Review for a National Institutes of Health Pathways to Prevention Workshop Cynthia Feltner , Kristina Peterson , Rachel Palmieri Weber , Laurie Cluff , Emmanuel Coker-Schwimmer , Meera Viswanathan , and Kathleen N. Lohr Metrics Cited byDiet, physical activity, and emotional health: what works, what doesn't, and why we need integrated solutions for total worker healthClearing the Smoke Screen: Smoking, Alcohol Consumption, and Stress Management Techniques among Canadian Long-Term Care WorkersAn Integrative Total Worker Health Framework for Keeping Workers Safe and Healthy During the COVID-19 PandemicHealth Risk CalculatorIntegrating worksite health protection and health promotion: A conceptual model for intervention and research 16 August 2016Volume 165, Issue 4Page: 294-295KeywordsBehaviorDisclosureHealth careHealth economicsHealth promotionMedical risk factorsPopulation statisticsPrevention, policy, and public healthSocial welfareSystematic reviews ePublished: 31 May 2016 Issue Published: 16 August 2016 PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".