Understanding Mental Health Service User Experiences of Restraint through Debriefing: A Qualitative Analysis
Bibliographic record
Abstract
OBJECTIVE: To examine debriefing data to understand experiences before, during, and after a restraint (seclusion, chemical, and physical) event from the perspective of inpatients at a large urban mental health and addiction hospital. METHOD: Audits were conducted on a purposeful sample of inpatient charts containing post-restraint event inpatient debrief forms (n = 55). Qualitative data from the forms were analyzed thematically. RESULTS: Loss of autonomy and related anger, conflict with staff and other inpatients, and unmet needs were the most common factors precipitating restraint events. Inpatients often reported that increased communication with staff could have prevented restraint. Inpatients described having had various negative emotional states and responses during restraint events, including fear and rejection. Post-restraint, inpatients often desired to leave the unit for fresh air or to engage in leisure activities. CONCLUSIONS: To our knowledge, our study is the first to use debriefing form data to explore mental health inpatients' experiences of restraint. Inpatients view restraint negatively and do not experience it as a therapeutic intervention. Debriefing, guided by a form, is useful for understanding the inpatient's experience of restraint, and should be used to re-establish the therapeutic relationship and to inform plans of care. In addition, individual and collective inpatient perspectives should inform alternatives to restraint.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".