Development and maturation of the occupational health services research field in the United States over the past 25 years: Challenges and opportunities for the future
Bibliographic record
Abstract
Work is an important social determinant of health; unfortunately, work-related injuries remain prevalent, can have devastating impact on worker health, and can impose heavy economic burdens on workers and society. Occupational health services research (OHSR) underpins occupational health services policy and practice, focusing on health determinants, health services, healthcare delivery, and health systems affecting workers. The field of OHSR has undergone tremendous expansion in both definition and scope over the past 25 years. In this commentary, focusing on the US, we document the historical development and evolution of OHSR as a research field, describe current doctoral-level OHSR training, and discuss challenges and opportunities for the OHSR field. We also propose an updated definition for the OHSR field: Research and evaluation related to the determinants of worker health and well-being; to occupational injury and illness prevention and surveillance; to healthcare, health programs, and health policy affecting workers; and to the organization, access, quality, outcomes, and costs of occupational health services and related health systems. Researchers trained in OHSR are essential contributors to improvements in healthcare, health systems, and policy and programs to improve worker health and productivity, as well as equity and justice in job and employment conditions. We look forward to the continued growth of OHSR as a field and to the expansion of OHSR academic training opportunities.
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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 | 0.133 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".