EFFECT OF OCCUPATIONAL STRESS ON BURNOUT AND ALEXITHYMIA AMONG MENTAL HEALTH PROFESSIONALS
Bibliographic record
Abstract
Background: The present study adds to the literature of occupational stress, burnout and alexithymia by examining the effect of occupational stress on burnout and alexithymia among mental health professionals. Method: A purposive sample of 100 mental health professionals which included 50 psychologists and 50 psychiatrists completed the questionnaire that included demographic sheet asking age, gender, qualification, years of experience, monthly income, and hospital sector and Maslach Burnout Inventory, Toronto Alexithymia Scale, and Occupational stress scale. Results: The data was analyzed using correlation, regression and t test. The regression findings illustrate that occupational stress is a significant predictor of burnout and alexithymia. The t-test findings illustrate that there is high occupational stress in male mental health professionals (41.82±4.52) than female (38.94±6.55) and the mean scores on burnout shows higher burnout tendency in male mental health professionals (87.84±5.22) than female (79.08±9.19). It also illustrate that there is high burnout in psychiatrist (88.68±7.89) than psychologist (82.90±10.45) and higher mean score difference on alexithymia in psychiatrists (81.74±5.00) than psychologists (77.14±8.72). Conclusion: The purpose of conducting this research was to spread awareness among the mental health professionals to care after their physical and mental health as they are the care taker of individual suffering from emotional distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".