Diverging views between clinicians, service users, family caregivers and researchers on the classification of restrictive practices in mental health services
Bibliographic record
Abstract
Abstract Aims Efforts to reduce restrictive practices (RPs) in mental health care are growing internationally. Yet, inconsistent definitions and perspectives often challenge the consistent implementation and evaluation of reduction strategies. This study explored which scenarios different mental health stakeholders classify as RPs, examined the contextual factors influencing these classifications and compared classification patterns across clinicians, researchers, service users and family caregivers. Methods An international cross-sectional survey was conducted using a multilingual online questionnaire hosted on the Qualtrics platform. A total of 851 stakeholders participated, including clinicians ( n = 517), service users ( n = 80), family caregivers ( n = 89) and researchers ( n = 165). Participants were presented with 44 potential RP case scenarios and asked to rate whether each scenario should be classified as an RP using a four-point Likert scale (Definitely yes, Probably yes, Probably no, Definitely no). The scenarios were organized into 22 paired comparisons, each sharing the same core context but differing in specific details. Paired comparisons were analyzed one pair at a time, allowing us to identify classification patterns between the scenarios and isolate the effects of particular contextual factors using ordered logistic regression. Interaction analyses were then conducted to assess how classification patterns varied across stakeholder groups. Results Substantial discrepancies exist both within and between stakeholder groups regarding whether a given action should be considered an RP or not. Physically visible actions were often identified as RPs across all groups, while less visible forms often went unrecognized. Contextual differences, such as the healthcare professional’s intention, duration of the action, methods used, presence or absence of consent, door-locking status, and the severity of anticipated harm to be prevented influenced whether a given action was classified as an RP. Service users classified more scenarios as RPs than other groups; however, their decisions were more context-sensitive, shifting notably even with minor changes in scenario details. Among the 22 paired scenarios compared, 13 (59.09%) showed significant differences ( p < 0.01) within at least one stakeholder group and eight demonstrated differences between groups. Conclusions Mental health stakeholders’ interpretations of RPs were often shaped not only by the inherent coercive nature of actions but also by the context in which they occurred and the professional role of the assessors. This underscores the need for harmonized definitions and classification frameworks for RPs, co-designed with diverse stakeholders. Addressing less visible forms of RPs in policy and clinical practice is also essential.
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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 | 0.080 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".