Exploring Public Health Research for Corporate Health Policy: Insights for Business and Society Scholars
Bibliographic record
Abstract
Despite the growing interest in societal impact in the business and society literature, there remains a notable gap in research on the impact of health interventions on physical and mental health and social welfare. To address this gap, we shift the unit of analysis to the intervention, akin to the level of analysis used in health research. Drawing on a curated subset of health interventions in the workplace from the public health literature, we argue that management scholars can adopt the methods used by public health scholars to design and assess health interventions. By tapping into the rich insights gained from these studies, management scholars can propose evidence-based interventions and policies that can enhance health outcomes, improve productivity, and cultivate a healthier workplace atmosphere. Collaborating with public health researchers, business and society scholars can help create a new field of research on corporate health policy.
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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 | 0.049 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".