Burnout and its Relationship to Psychological Distress and Job Satisfaction Among Academician and Non-Academician in Malaysia
Bibliographic record
Abstract
ObjectivesThe purpose of this study was to ascertain the prevalence of burnout and its associated risk factors among the University staff involving both academician and non-academician and relate these to their job satisfaction.MethodsA cross sectional study was conducted among the Universiti Teknologi MARA (UITM) staff involving both academician and non-academician. The participants were emailed the questionnaires through their university email and alternative email addresses. They were asked to complete questionnaires on their sociodemographic and work details, Copenhagen Burnout Inventory (CBI), Depression, Anxiety and Stress Scale (DASS-21) and Job Satisfaction Scale (JSS).ResultsAmong the 411 participants who participated, 53% were academicians (n= 216). Academician demonstrated greater burnout levels and psychological distress when compared to non-academician. Correlational analyses indicated moderate to high correlation between psychological distress and burnout due to work, personal and client where higher burnout was associated with higher psychological distress. Non-academician demonstrated greater job satisfaction levels when compared to academician. Correlational analyses indicated high correlation between job satisfaction and burnout due to work, with higher burnout levels associated with lower job satisfaction among staff. Conclusion This study showed that academicians suffers from high levels of burnout in aspects of personal, work and client related matters and this has contributed to higher psychological distress among them and greatly affect their job satisfaction.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".