Self-Management at Work’s Moderating Effect on the Relations Between Psychosocial Work Factors and Well-Being
Bibliographic record
Abstract
Mental health self-management (MHS) strategies may help workers with mental health concerns preserve and enhance their well-being. However, little research has explored how these strategies may help mitigate the effects of negative psychosocial work factors (PWFs) on well-being outcomes. This cross-sectional study investigated (1) the relationship between PWFs and well-being, (2) the association between MHS at work and well-being, and (3) the moderating role of self-management in preventing negative PWFs' deleterious effects. A sample of 896 Francophone workers in Canada completed a questionnaire that included self-reported measures related to workplace, self-management, and well-being. Structural equation modeling (conducted via the MPlus software, version 8.6) revealed that psychological demands were negatively related to positive well-being outcomes and positively associated with adverse well-being outcomes. Competency-related autonomy was positively associated with flourishing, and recognition was positively associated with flourishing and positive well-being at work, as well as being negatively associated with burnout and depression. Surprisingly, supervisor support was negatively related to positive well-being and positively related to burnout and depression. MHS was positively associated with positive well-being at work, flourishing, and work performance, but had no relationship with negative mental health. MHS significantly moderated the relationship between each PWF and well-being at work in both beneficial and adverse ways, depending on the specific well-being indicator being considered. From a workplace well-being perspective, this suggests that although self-management may help workers preserve and enhance their positive well-being, organizations must also directly target PWFs to prevent negative well-being outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".