Occupational Factors and Faculty Well-Being: Investigating the Mediating Role of Need Frustration
Bibliographic record
Abstract
Research evidence suggests that faculty members’ well-being is a serious concern in academia. However, little is known about the academic job demands and resources, which are specific to the professional context and faculty work, that influence faculty well-being. Moreover, the psychological processes how job characteristics lead to well-being outcomes among faculty are to date underexamined. We addressed these gaps and investigated the mediating impact of frustration of basic psychological needs on the relationship between academic job factors and faculty well-being. Survey data were collected from 592 faculties employed in 13 Canadian research-intensive universities. The questionnaire measured faculty perceptions of a) pressure and support in relation to academic tasks b) work-home conflict, c) workplace frustration of psychological needs, and d) varied well-being outcomes (engagement, commitment, burnout, and health). Structural equation modeling showed that work-home conflict and low academic resources positively predicted burnout and health problems but negatively predicted engagement. Work-home conflict, academic pressure, and insufficient support predicted greater basic need frustration that in turn negatively influenced faculty well-being. Findings highlight the need for future research to better identify aspects of academic work that obstruct or sustain faculty basic needs in order to provide need supportive academic culture that bolsters faculty well-being.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".