Burnout in community mental health nurses: findings from the all‐Wales stress study
Bibliographic record
Abstract
Stress and its outcomes are significant problems for mental health workers. Questionnaires were sent to 614 community mental health nurses (CMHNs) in Wales. Three hundred and one responded (49%). Of these, 283 completed the Maslach Burnout Inventory (MBI) (Maslach et al. 1996). Half of those who responded indicated that they were emotionally overextended and exhausted by their work. One quarter of respondents were found to possess negative attitudes towards their clients, and approximately one in seven experienced little or no sense of satisfaction with their work. Working in an urban environment and lacking a supportive line manager were indicators for higher emotional exhaustion. CMHNs were significantly more likely to have negative attitudes towards their clients if they: were male; worked with an elderly care caseload; lacked job security; and had an unsupportive line manager. However, CMHNs who had worked longer within the field of community mental health were more likely to have positive attitudes towards their clients. Those CMHNs who had not completed a specialist postqualifying education course and those who did not hold a supervisory or management position were found to have a lowered sense of personal satisfaction in their work. Those CMHNs who reported that they drank alcohol were more satisfied with their sense of personal accomplishments achieved in their work.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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 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".