Occupational identity, work, psychological distress and gender in management: results from SALVEO study
Bibliographic record
Abstract
Purpose This paper aims to examine the contribution of occupational identity and gender in explaining psychological distress among managers. It proposes and tests empirically a theoretical model that integrates identity theory into occupational stress and gender research. It analyses the proposition that a low level of verification of role identity is associated with a high level of psychological distress and that gender plays a moderating role in the relationship between role identity verification and psychological distress. Design/methodology/approach Multilevel regression analyses were conducted on a sample of 314 managers employed in 56 Canadian firms. Findings Low level of verification of one standard of managers’ role identity, namely, recognition, is significantly associated with managers’ psychological distress. It encloses monetary and non-monetary recognition, career prospects and job security. Notwithstanding, gender does not moderate the relationship between identity verification and psychological distress. Originality/value Studies addressing the contributions of identity and gender in the explanation of managers’ psychological distress are sparse. This paper helps to expand the scope of management and workplace mental health research as well as gender-related research, by proposing a new approach for the study of managers’ psychological distress, by the integration of identity theory and the analysis of the moderating role of gender.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".