Results of a Canadian study examining the prevalence and potential for developing compassion fatigue and burnout in radiation therapists
Bibliographic record
Abstract
Abstract Objective Caring is a fundamental tenet of healthcare. Caring ‘too much’ can result in compassion fatigue syndrome and is often linked to burnout and low morale. The objective of this study was to examine compassion fatigue, secondary traumatic stress (STS) and burnout by investigating the relationship between levels of compassion (compassion satisfaction) and STS and burnout. The study also aimed to identify radiation therapist (RTTs) groups who may be at risk for developing (STS) and burnout. Finally, we investigated the level of social support that RTTs receive. Methods RTTs practicing across Canada were invited to participate in an electronic questionnaire. The questionnaire consisted of: demographic information including health-related issues and occupational variables; the Professional Quality of Life Compassion Satisfaction and Fatigue Questionnaire (ProQOL-V) to assess the potential for compassion satisfaction and vulnerability for STS and burnout; and the Multidimensional Scale of Perceived Social Support (MSPSS) to examine the level and sources of social support. A two-way ANOVA was performed to test the statistical significance between varying groups within the study population. A linear regression analysis using potential co-factors was used to test correlations between compassion fatigue, compassion satisfaction and burnout and variables in age, education, years of experience and levels of caring to patients. Results A total of 477 survey responses were received representing a 36% response rate. Results of the regression analyses generally indicate inverse correlations between the risks associated with compassion satisfaction, burnout and STS compared with the independent study variables of age, education, years of experience and levels of caring to patients. It was observed that responses were not linear within subgroups (age groups, education classifications, years of study). Conclusion RTTs practicing in Canada have a substantial social support network and demonstrate high levels of compassion satisfaction in their daily practice. The results of the study indicate that compassion levels are inversely correlated with burnout and compassion fatigue, although some groups may be at higher risk than others. A possible risk catalyst for compassion fatigue and burnout is associated with underdeveloped managerial workplace support programmes.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".