Variables Associated With the Subjective Experience of Coercive Measures in Psychiatric Inpatients: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This systematic review presents evidence regarding factors that may influence the patient's subjective experience of an episode of mechanical restraint, seclusion, or forced administration of medication. METHOD: Two authors searched CINAHL, PubMed, SCOPUS, Web of Science, and Psych-Info, considering published studies between 1 January 1992 and 1 February 2016. Based on the inclusion criteria and methodological quality, 34 studies were selected, reporting a total sample of 1,869 participants. RESULTS: The results showed that the provision of information, contact and interaction with staff, and adequate communication with professionals are factors that influence the subjective experience of these measures. Humane treatment, respect, and staff support are also associated with a better experience, and debriefing is an important procedure/technique to reduce the emotional impact of these measures. Likewise, the quality of the working and physical environment and some individual and treatment variables were related to the experience of these measures. There are different results in relation to the most frequently associated experiences and, despite some data that indicate positive experiences, the evidence shows such experiences to be predominantly negative and frequently with adverse consequences. It seems that patients find forced medication and seclusion to be more tolerable than mechanical restraint and combined measures. CONCLUSIONS: It appears that the role of the staff and the environmental conditions, which are potentially modifiable, affect the subjective experience of these measures. There was considerable heterogeneity among studies in terms of coercive measures experienced by participants and study designs.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".