Co‐Designing Case Scenarios and Survey Strategies to Examine the Classification and Reporting of Restrictive Care Practices in Adult Mental Health Inpatient Settings: Perspectives From International Stakeholders
Bibliographic record
Abstract
There is a global initiative to reduce the use of restrictive care practices in mental health settings. Variations in the reported rates across regions complicate the understanding of their use and tracking trends over time. However, it remains unclear whether these discrepancies reflect real differences in the implementation of these practices or are sourced from inconsistencies in incident classification and reporting methods. This study employed a co-design approach to identify contexts that would influence the classification and reporting of restrictive care practices. The research involved 29 mental health stakeholders, including 22 professional experts from 13 countries across Europe, Africa, North America, Asia and Australasia and seven service users and family carers from Australia. Recruitment was conducted through email invitations, snowball sampling and social media outreach. Six web-based panel meetings, each lasting 90-120 minnutes were held. These discussions focused on exploring various contexts that might lead to uncertainty among professionals when classifying and reporting actions whether or not as restrictive care practices. A final list of 23 contexts was identified and considered for the development of 81 case scenario items. Finally, all the 29 panel members selected 44 from 81 case scenarios for inclusion in an upcoming international survey to examine variations in the classification and reporting of restrictive care practices. The findings from this co-design work emphasise the involvement of a wide range of factors and contexts in the classification and reporting of restrictive care practices that may contribute to the observed variations in the in the reported rates of these practices. The case scenarios developed in this study will support future research and serve educational purposes, illustrating real-life situations in the mental healthcare context.
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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 | 0.176 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".