Increasing coping and strengthening resilience in nurses providing mental health care: Empirical qualitative research
Bibliographic record
Abstract
BACKGROUND: Research on coping and resilience is on the rise. However, there is a paucity of information addressing strengths, assets, competence or resilience that enable nurses to remain committed and cope in their profession despite the adversities they face in their working environment. OBJECTIVE: The purpose of this research was to explore and describe how to strengthen the resilience of nurses in a work environment with involuntary mental health care users. METHOD: An exploratory and descriptive research design, which is contextual in nature, was used. RESULTS: Narrative responses to two open-ended questions (How do you cope with providing mental health care to involuntary admitted mental health care users? and; How can your resilience be strengthened to provide mental health care to involuntary mental health care users?) yielded coping mechanisms and resilience strengthening strategies. CONCLUSION: Nurses caring for involuntary mental health care users are faced with challenging situations while they themselves experience internal conflict and have limited choices available to be assertive. To strengthen their resilience, the following factors should be taken into account: support, trained staff, security measures and safety, teamwork and in-service training and education.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".