RECONSTRUCTING PROFESSIONAL COMMITMENT: EMOTIONAL EXHAUSTION AND RESILIENCE OF UNIVERSITY LECTURERS UNDER ACADEMIC STRAIN
Bibliographic record
Abstract
The professional commitment of lecturers in higher education is increasingly under pressure due to publication demands, administrative burdens, and continuous performance-based evaluations. This condition triggers emotional exhaustion, not only in physical exhaustion but also in the depletion of psychological energy and a reduction in the depth of work’s meaning. This research aims to understand the experience of emotional fatigue and the role of psychological well-being as a resilience mechanism in maintaining professional commitment. Using the qualitative Interpretative Phenomenological Analysis (IPA) approach, data were collected through in-depth interviews, observations, and documentation of lecturers from various universities. The results show that emotional exhaustion shifts professional involvement toward a more mechanistic, defensive stance. In contrast, psychological well-being, through self-acceptance, life goals, autonomy, personal growth, and positive relationships, can reconstruct the profession’s meaning and maintain commitment. This research contributes by presenting a resilience model based on psychological well-being in an academic context. So that means universities need to develop policies that not only reduce workload but also strengthen lecturers’ psychological foundations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".