An International Perspective on Worker Mental Health Problems: Who Bears the Burden and How are Costs Addressed?
Bibliographic record
Abstract
OBJECTIVE: To discuss the burden of poor mental health in workers, who currently bears it, and how the associated rising costs are being addressed, from an international perspective. METHOD: We identify the stakeholder groups and the costs they incur as a result of problems related to mental health in 6 different domains. In addition, we offer examples of programs, services, and strategies being used to either decrease costs or enhance benefits. RESULTS: Mental illness is associated with a wide range of costs distributed across multiple stakeholders including government, employers, workers and their families, and the health care system. The costs incurred by the groups are interrelated; an attempt to decrease the burden for one group of stakeholders will inevitably affect other stakeholders. Thus the answer to the question of who bears the costs of poor mental health is "everyone." CONCLUSIONS: Everyone could benefit from investment in improved mental health in the workplace. However, because the benefits associated with improved worker mental health are often distributed among several stakeholders, the incentives for any single stakeholder to pay for additional services for workers may be diluted. As a consequence, no one invests. Nevertheless, there is a role for all stakeholders, just as there are potential benefits for all. Along with government, employers, employees, and the health care system must invest in promoting good workplace health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".