OC-0562: Optimizing teamwork in radiation therapy: A Canadian experience
Bibliographic record
Abstract
2 nd ESTRO Forum 2013 S215 satisfaction, incidents, stress and burnout, professional development, workload, retention and turnover.All questions were taken from validated instruments or adapted from the 'NHS Staff Survey'.The survey included two validated tools to measure job satisfaction and burnout, and was based on validated tools including NHS staff survey 1 , Maslach Burnout Inventory 2 , JobSatisfaction Scale 3 .The sample was recruited from all Radiotherapy professionals using an open survey and a range of activities, including the UK professional bodies representing RTTs, Physicists, dosimetrists and technicians.Results: 658 completed responses were returned, representing aresponse rate of ~18%.A statistically significant difference was seen in distribution of mean job satisfaction scores and its aspects across professional groups and treatment centres.The radiotherapy workforce demonstrate higher levels of emotional exhaustion, depersonalization, and low personal accomplishment as compared to health care workers outside of radiotherapy and oncology, and non-health care occupations. Conclusions:The UK health service is undergoing significant organisational changes; an increased provision of radiotherapy is required, while also delivering the appropriate treatment and care indicated by the evidence base.Organisations and managers will be required to adopt strategies to combat the effects of reforms to pay and contractual benefits.Maintaining and improving morale and job satisfaction will be a key success factor in service delivery.Implementing strategies and equipping the radiotherapy workforce with skills to be resilient to the effects of stress and burnout in order that the optimum treatment package can be delivered for patients.1.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".