When Could a Stigma Program to Address Mental Illness in the Workplace Break Even?
Bibliographic record
Abstract
OBJECTIVE: To explore basic requirements for a stigma program to produce sufficient savings to pay for itself (that is, break even). METHODS: A simple economic model was developed to compare reductions in total short-term disability (SDIS) cost relative to a stigma program's costs. A 2-way sensitivity analysis is used to illustrate conditions under which this break-even scenario occurs. RESULTS: Using estimates from the literature for the SDIS costs, this analysis shows that a stigma program can provide value added even if there is no reduction in the length of an SDIS leave. To break even, a stigma program with no reduction in the length of an SDIS leave would need to prevent at least 2.5 SDIS claims in an organization of 1000 workers. Similarly, a stigma program can break even with no reduction in the number of SDIS claims if it is able to reduce SDIS episodes by at least 7 days in an organization of 1000 employees. CONCLUSIONS: Modelling results, such as those presented in our paper, provide information to help occupational health payers become prudent buyers in the mental health market place. While in most cases, the required reductions seem modest, the real test of both the model and the program occurs once a stigma program is piloted and evaluated in a real-world setting.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".