Best Practices in Workplace Mental Health: An Area for Expanded Research
Bibliographic record
Abstract
Mental health, mental illness and the workplace is a timely topic. Many Canadian employers are recognizing the consequences for their bottom line of not addressing the issue. Dr. Stuart's paper was a thorough discussion of the topic of stigma. I suggested four points for consideration. First, the time may have come to substitute the word discrimination for stigma. It opens a rights and responsibilities dialogue that would be valuable. Also, employers and employees understand the term. Second, there are two populations to consider; those who want to enter the workforce, possibly for the first time, and those who want to stay in the workforce. Studying both populations' needs and experiences would yield new knowledge. Third, consider broadening the investigation scope to include instances where things are working (best practices). And finally, concentration only on anti-stigma programs would exclude other innovations.
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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 | 0.057 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.016 | 0.041 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.075 | 0.042 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".