Levels of burnout among registrars and medical officers working at Bloemfontein public healthcare facilities in 2013
Bibliographic record
Abstract
Background: Burnout is prevalent among medical personnel and affects their work environment. This study investigated the level of burnout among registrars and medical officers at public healthcare facilities in Bloemfontein.Methods: An analytical cross-sectional study included registrars and medical officers at four public healthcare facilities in Bloemfontein. Socio-demographic information was collected and participants completed the Maslach Burnout Inventory, which consists of three subscales: emotional exhaustion, depersonalisation and personal accomplishment.Results: Of the 300 potential participants, 205 were included in the result. Only 3.4% of the participants showed no burnout on all three subscales and 28.3% had only low to moderate levels of burnout on all three subscales. A quarter (26.3%) of the participants showed high burnout on one subscale, but not the others. Furthermore, 26.3% showed a high level of burnout on any combination of two of the three subscales. A high degree of burnout on all three subscales was found in 15.6% of the participants.Conclusion: Burnout is a major problem among registrars and medical officers working in public hospitals in Bloemfontein. An action plan needs to be put in place in partnership with the Departments of Health and Higher Education to prevent burnout among an important working cadre.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".