Post-incident review after restraint in mental health care -a potential for knowledge development, recovery promotion and restraint prevention. A scoping review
Bibliographic record
Abstract
BACKGROUND: Use of physical restraint is a common practice in mental healthcare, but is controversial due to risk of physical and psychological harm to patients and creating ethical dilemmas for care providers. Post-incident review (PIR), that involve patient and care providers after restraints, have been deployed to prevent harm and to reduce restraint use. However, this intervention has an unclear scientific knowledge base. Thus, the aim of this scoping review was to explore the current knowledge of PIR and to assess to what extent PIR can minimize restraint-related use and harm, support care providers in handling professional and ethical dilemmas, and improve the quality of care in mental healthcare. METHODS: Systematic searches in the MEDLINE, PsychInfo, Cinahl, Sociological Abstracts and Web of Science databases were carried out. The search terms were derived from the population, intervention and settings. RESULTS: Twelve studies were included, six quantitative, four qualitative and two mixed methods. The studies were from Sweden, United Kingdom, Canada and United States. The studies' design and quality varied, and PIR s' were conducted differently. Five studies explored PIR s' as a separate intervention after restraint use, in the other studies, PIR s' were described as one of several components in restraint reduction programs. Outcomes seemed promising, but no significant outcome were related to using PIR alone. Patients and care providers reported PIR to: 1) be an opportunity to review restraint events, they would not have had otherwise, and 2) promote patients' personal recovery processes, and 3) stimulate professional reflection on organizational development and care. CONCLUSION: Scientific literature directly addressing PIR s' after restraint use is lacking. However, results indicate that PIR may contribute to more professional and ethical practice regarding restraint promotion and the way restraint is executed. The practice of PIR varied, so a specific manual cannot be recommended. More research on PIR use and consequences is needed, especially PIR's potential to contribute to restraint prevention in mental healthcare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".