Who cares for the carers? Development of an educational resource: identification, prevention and mitigation of burnout for psychiatric nurses in Newfoundland and Labrador Health Services
Bibliographic record
Abstract
Background: Psychiatric nurses are often exposed to critical and traumatic events, as well as workplace violence, which can lead to burnout. This can place a substantial burden on patients, nurses themselves, and therefore, the whole health care system. Few interventions and resources have been studied or are available in practice settings to assist nurses in identifying, preventing and mitigating burnout. Purpose: To develop an educational resource to assist psychiatric nurses in identifying, preventing and mitigating burnout. Methods: A comprehensive literature review, an environmental scan, and consultations were conducted to inform the development of an educational resource to assist psychiatric nurses in identifying, preventing and mitigating burnout. Results: The literature review revealed contributing factors to psychiatric nurse burnout and interventions to address psychiatric nurse burnout. The environmental scan yielded few useful resources, yet informed the inclusion of available content, while the consultations identified psychiatric nurses’ perceptions on burnout and informed the educational modality and content preferences. Conclusion: A pocketbook educational resource was developed for psychiatric nurses to aid in identifying, preventing and mitigating psychiatric nurse burnout. A plan to advertise the pocketbook resource at the Western Memorial Regional Hospital will be established.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".