Bibliographic record
Abstract
The social and physical conditions under which people work have been demonstrated in several studies to have a direct impact on disease, injury, disability and health-related outcomes in workers.Of increasing interest is the relationship between mental health and conditions at work and the related economic, social, legal and health-related consequences.In their review of the literature, Dewa and colleagues (2010) noted, mental health AbstrAct A complex topic like workplace mental health requires multidisciplinary, multi-sectoral, mixed methods research and effective knowledge translation of research findings.In this commentary, two of the 13 institutes that comprise the Canadian Institutes of Health Research -the institute of Gender and Health and the Institute of Population and Public Health -discuss strategies for advancing research on mental health and the workplace.With a focus on each Institute's mandate, the commentary argues that there is a need to advance our understanding of how biological, social, cultural and environmental determinants of workplace mental health are influenced by sex and gender, and of how population health intervention research can generate evidence that will strengthen the impact of workplace interventions to reduce mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".