Risk of burnout among radiographers in a large tertiary care hospital in Saudi Arabia
Bibliographic record
Abstract
Objective: Radiographers are known to be at increased risk of burnout due to the emotionally taxing interactions that they have with their patients on a daily basis. The aim of this study was to assess the risk of burnout among radiographers in a large tertiary care hospital in Saudi Arabia.Methods: This was an observational, cross-sectional study using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS). This tool has been extensively tested and validated. 150 full-time radiographers at King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia were invited. Trainees, interns and on job trainees (OJT) were excluded to ensure sample homogeneity. Results: 150 participants were invited to participate in the questionnaire with response rate 142 (95%). 70 participants (49%) were male and 72 (51%) female. Maslach Burnout Inventory-Human Services Survey subscale results: The mean (± SD) score for emotional exhaustion, depersonalization and personal accomplishment were 21.44 (± 13.0), 8.12 (± 6.99) and 35.63 (± 8.59) respectively. Moderate to high risk of burnout for emotional exhaustion, depersonalization and personal accomplishment were reported in 67%, 52% and 58% of participants respectively. Conclusions: 67% of radiographers were at moderate to high risk of burnout for emotional exhaustion, 52% for depersonalization and 58% for personal accomplishment. Policymakers should take necessary steps to recognize factors contributing to staff burnout and take appropriate steps to improve the work environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".