Leadership et santé et mieux-être au travail : quelles leçons peut-on tirer pour les travailleurs en assignation internationale?
Bibliographic record
Abstract
La présente étude vise à dresser un survol des principaux constats tirés des revues systématiques et des méta-analyses portant sur l’effet du leadership sur la santé mentale et le mieux-être au travail, et de proposer une analyse critique de leur application au contexte des travailleurs expatriés. Les bases de données Medline, EMBASE, EBM et Web of Knowledge ont été consultées avec des mots-clés spécifiques au leadership, à la santé mentale et au mieux-être. Les résultats montrent que malgré l’augmentation des études examinant les effets du leadership sur la santé mentale en contexte organisationnel, la problématique demeure sous-étudiée en gestion internationale des ressources humaines. À cet effet, des recommandations sont formulées afin que les superviseurs soient mieux outillés pour répondre aux situations dans un contexte où leurs subordonnés sont déployés à l’étranger.
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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 | 0.045 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".