Burnout and the provision of psychosocial care amongst Australian cancer nurses
Bibliographic record
Abstract
Purpose To assess the prevalence of burnout amongst Australian cancer nurses as well as investigate the systemic and individual factors associated with burnout, including training and supervision for nurses in psychosocial care. Burnout amongst cancer nurses can have serious consequences for the individual nurse, the hospital and patients. Psychosocial care has been demonstrated in many studies to reduce distress in cancer patients; however, previous studies have suggested that providing psychosocial care can be stressful if nurses feel they lack appropriate training. Psychosocial skill training and supervision may be a way of improving job satisfaction and reducing burnout amongst nurses. Method Two hundred and thirty cancer nurses were recruited between November 2010 and April 2011 and completed an online questionnaire. Results Burnout levels within this population were found to be below nursing norms. Adequacy of training and supervision, frequency of supervision and percentage of role spent managing psychosocial care were found to be associated with burnout. Workload, Control, Reward and Community were independent predictors of burnout, and nurses with a greater mismatch in these areas identified as having High levels of burnout. Conclusions Strategies to reduce burnout include providing cancer nurses with a varied and sustainable workload, awarding financial and social recognition of efforts and encouraging nurses to develop a sense of control over their work. Providing regular training and supervision in psychosocial care that is perceived to be adequate may also assist in reducing 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".