Exploring Mental Health and Well-Being Among University Faculty Members: A Qualitative Study
Bibliographic record
Abstract
The current exploratory qualitative study describes how environmental factors, social interactions, personal experiences, and stigma affect mental health and help-seeking. In-depth, semi-structured interviews were conducted with nine university faculty members who self-identified as having mental illness–related concerns. Using Bronfenbrenner's ecological systems framework and thematic analysis, four domains were determined: (1) macrosystem (i.e., influences of academic culture); (2) mesosystem (i.e., influences of faculty leadership and interpersonal dynamics); (3) microsystem (i.e., influences of individual mental health experiences); and (4) exosystem (i.e., influences of stigma across structural, interpersonal, and intrapersonal levels). These domains included barriers to and facilitators of mental health and help-seeking. Findings suggest that competitiveness and individualism may perpetuate stereotypes that mental illnesses are inherent weaknesses, and that seeking help is a barrier to academic success. Recommendations for future research are provided. [ Journal of Psychosocial Nursing and Mental Health Services, 60 (11), 17–25.]
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".