Healthy universities: Exploring the relationship between psychosocial needs and work-related health among university employees
Bibliographic record
Abstract
The present study explores psychosocial needs among university employees and the extent to which these needs influence employee perceptions of how work positively or negatively affects their health. Structural equation modeling (SEM) analyses among Norwegian faculty members (N = 11,533) suggest that needs differ in importance to the two work-related health outcomes. Multi-group analyses suggest gender differences in the level of these needs and in their degree of relationship with positive/negative work-related health. Among women, the strongest predictors of positive and negative work-related health are work engagement and autonomy, respectively. Among men, the strongest predictors of positive and negative work-related health are meaning and social community, respectively. Although significant differences were found in the level of the psychosocial needs across different university groups (faculty, PhD students, administrative/technical staff), their predictive value for how work affects their health positively or negatively is basically equivalent across groups. Study findings raise two implications: (1) the mechanisms and characteristics of the work environment that promote versus detract from health in the university setting do not appear to be two sides of the same coin and suggest different sets of interventions for improving employee health, and (2) gender differences should be taken into account in designing interventions to improve health and well-being in universities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".