[Supporting the return to work following sick leave for a depressive disorder: why and how?]
Bibliographic record
Abstract
Depressive disorders have become one of the main causes of sick leave in recent years. For the individuals concerned, the risk of long-term work disability is very real and generates considerable human and social costs, not to mention financial costs. Despite its importance, the issue of the return to work is rarely presented to primary care professionals, among others, as a priority to be factored into their clinical interventions. The latter thus have very few guidelines to help them choose the best intervention for promoting a timely return to work and may even question the relevance of such an objective.The purpose of this article is therefore to propose a set of reference points for primary care professionals by answering the following question: why and how should the return to work be supported following sick leave for a depressive disorder? The first part of the article provides an overview of current knowledge that supports the relevance of early intervention to the prevention of long-term work disability. The second part proposes a number of promising interventions for achieving this objective and feasible for primary care professionals. These proposals are based on recent research work by our team.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".