Bibliographic record
Abstract
Abstract: Objective: The study investigates the workplace-related determinants of academic faculty burnout in health professions institutions. Methods: This mixed-methods study employs both quantitative and qualitative approaches. We invited 154 academic faculty members from health professions colleges to participate and measured burnout using the original 22-item Maslach Burnout Inventory (subscales: Emotional Exhaustion, Depersonalization, and Low Personal Accomplishment). A p-value of less than .05 is considered statistically significant. We used a direct content analysis approach to code the interview transcripts after transcription. Results: Out of the 96 participants (62%), 38 (39.6%) reported mild to moderate burnout, while 60.4% experienced significant burnout. Emotional Exhaustion (41.7%) was more prevalent than Personal Accomplishment (38.5%) and Depersonalization (DP) (25%) among most faculty members. A psychometric analysis showed a significant correlation ( p-value = .02) between burnout and education level. Our analysis identified four key factors influencing burnout: (a) workload, (b) lack of integrity in professional competence, (c) workplace stress, and (d) insufficient engagement in professional development activities leading to career dissatisfaction. Conclusion: Faculty members in health professions face high levels of burnout. Preventive measures and corrective programs to reduce burnout and enhance performance are recommended.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".