A longitudinal and comparative study of psychological distress among professional workers in regulated occupations in Canada
Bibliographic record
Abstract
BACKGROUND: Although several studies are concerned by the phenomenon of psychological distress at work, few studies have looked at the prevalence of psychological distress among professional workers in the regulated occupations and compare this prevalence with other occupations. OBJECTIVES: This study propose to define regulated occupations by laying out the theoretical boundaries that apply to the practice of these occupations and try to understand how regulated occupations contributed to the experience of psychological distress in the Canadian workforce over time. METHOD: Multilevel logistical regression analyses on longitudinal data were performed to compare the odds of experiencing psychological distress over time among professional workers in regulated occupations (n=276) and among other professional workers, classified into 6 categories (n=6731), over a 12-year period. RESULTS: The results show that proportion of distress in the workforce decreases for all occupations between Cycle 1 and Cycle 7 of the NPHS, but this decrease is not linear over time. The results show also that regulated occupations present a lower probability of psychological distress only when compared with white-collar workers. CONCLUSIONS: These results suggest that occupation contributes little toward understanding the prevalence of psychological distress in the Canadian workforce. Further research needs are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".