L’impact du champ de pratique et du secteur d’activité sur la détresse psychologique au travail : le cas des avocats québécois
Bibliographic record
Abstract
In Canada, 27% of workers consider most of their days extremely stressful. The consequences of this stress are onerous for individuals and organisations. The strong growth (nearly 400%) in requests for assistance addressed to the PAMBA in recent years demonstrates that the legal profession in Quebec is no exception to this trend. In this respect, lawyers are three times more likely to suffer from depression compared to the rest of the employed population. Faced with this alarming portrait, this doctoral thesis aims to answer the following main question: what role do the field of practice and the sector of activity play in the explanation of psychological distress at work (PDW) among Quebec lawyers? Two main objectives are pursued: 1) to validate an abbreviated scale of PDW; and 2) to identify the specific contribution of the field of practice and the sector of activity on PDW among Quebec lawyers, using a multidimensional approach. To do this, factorial analyses and hierarchical multiple regressions are carried out, from a secondary database (2086 participants) collected via a self-reported questionnaire comprising 44 key variables allowing a relevant conceptual coverage to the study of PDW in the context studied. Overall, the results confirm the importance of considering determinants coming from several spheres of the individual’s life when analyzing PDW and support the theoretical model adopted. Contrary to the literature, our results show that most of the variance of PDW is explained by working conditions. Also, it appears that the effect of these conditions on the PDW differs according to the field of practice in which the lawyers work. Finally, it seems that each field of practice is exposed to different risks arising from the working conditions that are specific to each of these fields, and which are likely to play a role in developing PDW.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".