Towards an Enhanced Understanding of Factors Involved in the Return-to-Work Process of Employees Absent due to Mental Health Problems
Bibliographic record
Abstract
The purpose of this study was to reach a better understanding of the factors involved in the work reintegration process among employees of the Quebec Civil Service who were absent from work because of a mental health problem, as certified by a medical doctor. A qualitative approach was used based on data obtained from interviews. Analysis of the data allowed researchers to reconstruct these people's experience at various stages of the process of work reintegration, their interpretation of these events, and their responses to the difficulties they met. The results support the notion that the inability to work and return to work are constructs that relate not only to employees' health, but also to management practices that influence the work reintegration process. Support from colleagues and superiors, improvement in the conditions that contributed to the work interruption, a progressive return to work, and better cohesion between absence management practices and support measures are key factors in ensuring that the employee returns to work and remains in the workplace.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".