Burnout in therapy radiographers in the UK
Bibliographic record
Abstract
The 2007 UK National Radiotherapy Advisory Group report indicated that the number and type of staff available is one of the "rate-limiting" steps in improving productivity in radiotherapy departments. Retaining well-trained, satisfied staff is key to meeting the objectives of the report; burnout is an important factor linked to satisfaction and attrition. The results of a survey measuring burnout in a sample of radiotherapists (therapy radiographers) are presented and considered against norms for the health sector and burnout in therapists from Canada and the USA. Case study methodology was used studying six radiotherapy departments selected because of close geographical proximity and differing vacancy rates for radiotherapists. An anonymous survey of radiotherapists used the Maslach Burnout Inventory (MBI) and other workforce-related measures (e.g. job satisfaction scales, measures of professional plateau, intentions to leave, job characteristics and demographic data); the results of the burnout questionnaire alone are presented in this paper. A total of 97 completed questionnaires were returned (representing a 28% response rate). The average score for emotional exhaustion was higher than the MBI norms, with 38% of respondents reporting emotional exhaustion (an element of burnout). The data presented support and validated a previous qualitative study, and highlighted key areas of concern requiring further study. A correlation between burnout and job dissatisfaction and intention to leave was identified; managers may want to consider encouraging role extension and good leadership qualities in treatment unit leaders to minimise the potential for burnout.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".