Évaluation du stress selon le questionnaire de Karasek, chez les fonctionnaires marocains
Bibliographic record
Abstract
This article addresses the assessment of occupational stress within several Moroccan ministerial departments, using Karasek's model, which analyzes stress through psychological demand, decision latitude, and social support.The study was conducted with 529 civil servants in administrative positions and reveals a predominance of the so-called "job strain" profile, characterized by high demand combined with low autonomy-an indication of a particularly concerning level of stress.The results highlight a low decision latitude for the majority of employees, reflecting a lack of autonomy in task execution, as well as insufficient social support, especially from hierarchical superiors.These findings are particularly pronounced among women and younger civil servants.The study also includes the translation into Arabic and the standardization of the Canadian-French version of the Karasek Job Content Questionnaire (JCQ), with the aim of adapting it to the Moroccan context.Based on the results obtained, concrete actions can subsequently be proposed to prevent work-related stress in the public sector.These actions could focus in particular on the implementation of more participatory management, the strengthening of continuing training, and the establishment of psychological support tailored to the organizational specificities of the Moroccan public sector.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".