Dilemmes des médecins traitants lors du retour au travail de personnes aux prises avec un trouble mental courant : illustration par des vignettes cliniques
Bibliographic record
Abstract
Introduction Common mental disorders (CMD) are one of the leading causes of workplace disability worldwide. Many studies show that the longer a person is on sick leave, the less likely they are returning to work. It is therefore important that the duration of a work absence be an adequate duration to allow the individual to make a lasting recovery while reducing the risk of relapse. Clinicians have an important role to play in the professional recovery of people with CMD. Purpose The main objective of this article is to present clinical cases supported by the literature related to the management of return to work by clinicians treating their patients with CMD. Methods Based on clinical observations, three clinical cases illustrating several dilemmas that clinicians may encounter when managing return to work of their patients with a CMD are presented. These dilemmas are supported by articles published between 2000 and 2020 from the Medline and PsycInfo databases. Results and discussion Three clinical cases address dilemmas related to the following themes: 1) the assessment of the therapeutic potential of work absence, 2) the expert role given to clinicians and the process of assessing work disability, 3) the administrative aspects related to this assessment and 4) the impact of this assessment on therapeutic alliance between the clinician and his/her patient with CMD. The literature tells us that these are recurring dilemmas for clinicians when managing the return to work of their patients with CMD. Conclusions There are several dilemmas in the management of sick leave among workers with CMD by clinicians. These dilemmas highlight, among other things, the importance for clinicians to work collaboratively with other stakeholders and to obtain their support and collaboration. These observations lead us to conduct a more systematic review of the experience of clinicians and their needs.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".