Development of a self-directed learning resource focused on the identification, treatment, and prevention of Nursing burnout for Nurses practicing in acute care settings
Bibliographic record
Abstract
Background: Burnout is an important healthcare issue that has negative consequences for nurses, patients, and the healthcare system. Feelings of nursing burnout can be characterized as emotional exhaustion, depersonalization, and reduced personal accomplishment. Nurses must be supported in improving their well-being and mitigating the impact of this insidious phenomenon. Purpose: The purpose of this practicum project was to develop a learning resource focused on the identification, treatment, and prevention of burnout for nurses practicing within acute care. Methods: (1) an integrative literature review; (2) an environmental scan of reputable websites, resources, policies, and programs supporting nursing burnout from health authorities within Atlantic Canada; (3) consultation interviews with key stakeholders; and (4) the development of a self-directed online learning resource focused on nursing burnout. Results: The literature revealed that burnout is a substantial issue to nurses and the healthcare system. Effective interventions that enhance resiliency and improve mindfulness for nurses can reduce the impact of burnout for nurses. The environmental scan revealed several reputable sources of information that were considered for topics explored within the learning resource. Consultation interviews confirmed the need for supportive resources for nurses related to nursing burnout. Based on these results, a self-directed learning resource was developed exploring the identification, treatment, and prevention of burnout. Conclusion: Burnout has long been associated with nurses. Having a learning resource to support nurses within the healthcare system will help to improve nurses well-being and mitigate feelings of nursing burnout.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".