545 Workplace practices and policies to support workers with depression
Bibliographic record
Abstract
Introduction The burden associated with the effects of depression in the workplace is extensive. Workers with depression lose more health-related productive time, have higher rates of absenteeism and short-term disability, and experience higher rates of job turnover than those without depression. Our objective is to synthesise evidence from the scientific literature, practice evidence (workplace policies and practices), and experiences from OHS stakeholders. Methods Our study sample is OHS stakeholders: workers, managers, and consultants in workplaces from our contact database (approximately 700 OHS contacts across Canada willing to be contacted for research). Data collection includes a web-based survey, focus groups (ongoing), and interviews (ongoing) with stakeholder representatives from multiple sectors. We are collecting information about workplace practices and policies to prevent productivity losses, promote stay-at-work, and support return-to-work for workers with depression. The synthesis is a two stage process: first synthesising practice evidence gathered from stakeholders and then combining that with evidence from the scientific literature. Results Preliminary results (n=400, 73% workers, 27% managers/consultants) reveal a willingness among participants to share their experiences with depression and work. Workers report practices related to non-judgemental listening and external supports were most helpful to them. Managers/consultants suggest non-judgemental listening and employee assistance programs were most helpful. However, workers often feel workplace support is lacking and report non-supportive supervisors as a key barrier to receiving needed support. Managers indicate a lack of training and knowledge about depression are the main barriers to providing support to workers. Workers did not feel providing information was helpful whereas managers often did. Conclusion Preliminary results reveal the importance of non-judgemental listening as a workplace support for depression. However, responses reveal workers and managers do not have similar experiences of workplace support. Synthesising practice and scientific evidence will help guide policies and practices to support workers with depression.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".