The experiences of nurses following seclusion or restraint use and immediate staff debriefing in inpatient mental health settings
Bibliographic record
Abstract
AIM: The aim of this study is to explore nurses' experiences of seclusion or restraint use and their participation in immediate staff debriefing in inpatient mental health settings. DESIGN: This research was conducted using a descriptive exploratory design and data were gathered through in-depth individual interviews. METHODS: The experiences of nurses following seclusion or restraint use and their participation in immediate staff debriefing were explored via teleconference, using a semi-structured interview guide. Reflexive thematic analysis was used to identify prevalent themes from the data. RESULTS: Interviews (n=10) were conducted with nurses from inpatient mental health wards in July 2020. Five themes emerged through the data analysis: (i) ensuring personal safety; (ii) grappling between the use of least-restrictive interventions and seclusion or restraint use; (iii) navigating ethical issues and personal reactions; (iv) seeking validation from colleagues and (v) attending staff debriefing based on previous experience. The themes were also analysed using Lazarus and Folkman's Transactional Model of Stress and Coping. CONCLUSION: Staff debriefing is a vital resource for nurses to provide and/or receive emotion- and problem-focused coping strategies. Mental health institutions should strive to establish supportive working environments and develop interventions based on the unique needs of nurses and the stressors they experience following seclusion or restraint use. PATIENT OR PUBLIC CONTRIBUTION: Nurses in both frontline and leadership roles were involved in the development and pilot test of the interview guide. The nurses who participated in the study were asked if they can be recontacted if clarification is needed during interview transcription or data analysis.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".