Survey of the degree of burnout in state hospitals among Durban and Pietermaritzburg anaesthetists in KwaZulu Natal, South Africa.
Bibliographic record
Abstract
Background: The most widely accepted model that defines the syndrome of burnout is Maslach Multidimensional Theory of Burnout. According to the theory burnout has three dimensions: emotional exhaustion (EE), depersonalisation (DP), and reduced personal accomplishment (PA). The primary aim of this study is to describe the degree of burnout experienced by anaesthetists working in the eThekwini Hospital Complex and Pietermaritzburg Metropolitan state hospitals in KwaZulu Natal in South Africa. Methods: The Maslach Burnout Inventory a well‐tested instrument with high reliability and validity was used as a measure of burnout with three subscales of emotional exhaustion, depersonalisation, and personal accomplishment. Results: Of the 160 anaesthetic care givers available, 139 completed the questionnaires giving a response rate of 86.8%. Burnout occurred in all participants that is medical officers (general medical doctors with a diploma in anaesthetics), registrars (general medical doctors with a diploma in anaesthetics or residents training and specializing in anaesthesiology) and consultants (specialists anaesthesiologists with a diploma and a fellowship in anaesthesiology). The prevalence of burnout was 17.9%. Of the total number of participants, greater proportion of anaesthetic care givers were female aged 31 to 40 years and that a substantial percentage had 4 to 8 years’ experience in anaesthesiology. The dimensional scores when considered individually showed that 42% of anaesthetists experienced high levels of emotional exhaustion, 38% reported high levels of depersonalisation and the lack of personal achievements was observed in more than 50% of the practising anaesthetists. Conclusion:. High levels of burnout were noted among anaesthetists in Kwazulu Natal. An association was noted between quality of sleep and emotional exhaustion and depersonalization. This information could be of value to the department of anaesthesiology as they are in position to implement interventions that promote participants well-being. Anaesthesiology practices should evaluate the balance between the demands they place on anaesthetic care providers and the resources provided to sustain an engaged, productive, and satisfied anaesthetic workforce. If the multiple stressors that lead to burnout are not addressed the department of anaesthesiology will be plagued by burnout.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".