Prevalence and predictors of burnout in critical care health workers in palestine
Bibliographic record
Abstract
Establish the prevalence and risk factors of burnout in critical care health workers in Palestine. Two surveys and a thematic analysis. The final Survey combined results of thematic analysis with Maslach Burnout Inventory. Health workers from critical care units in Palestine. Two cross-sectional surveys. Survey 1 established perceived risk factors of burnout. A thematic analysis was completed on Survey 1, to identify unique risk factors to be added to the Maslach burnout survey. The combination of the standard Maslach burnout survey, and unique risk factors identified in survey 1, made up survey 2. There were 141 survey respondents. Burnout levels were high in each of the three domains of the Maslach Burnout survey (occupational exhaustion 59.0%, depersonalization and loss of empathy 55.0, low level of personal accomplishment 58.0%). Higher scores on occupational exhaustion domain were found in respondents with higher education level (H statistic 12.582, p = 0.014), in physicians rather than nurses or other job category (H statistic 7.689, p = 0.021), in those respondents who felt they were not respected at work (H statistic 21.894, p = 0.001), those who felt burned out due to the economic situation in Palestine (H-statistic 12.977, p = 0.043), those who felt salary was too low (H-statistic 19.020, p = 0.004), those who felt stressed because of the high patient acuity in the intensive care unit (H-statistic 21.564, p = 0.002) and those who felt unsupported by colleagues at their work (H-statistic 28.259, p = 0.0001). Higher scores of burnout on the depersonalization and loss of empathy domain were found with higher education (H statistic 12.270, p = 0.015), in physicians rather than nurses or other job category (H statistic 8.406, p = 0.015), respondents who did not feel respected at work (H-statistic 22.625, p = 0.001), and those who felt unsupported by work colleagues (H-statistic 23.389, p = 0.0001). Higher scores on burnout on the personal accomplishment domain were found with never-married marital status (H statistic 7.107, p = 0.008), and with respondents who felt unsupported by work colleagues (H-statistic 12.959, p = 0.044). Health workers in critical care units in Palestine have high rates of burnout. Several factors unique to Palestine predict burnout. Further research is required to establish efficacy of interventions to improve burnout in critical care health workers in Palestine.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".