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Record W4404999045 · doi:10.1111/inm.13479

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

2024· article· en· W4404999045 on OpenAlexaff
Zelalem Belayneh, Den‐Ching A. Lee, Terry Haines, Deborah Oyine Aluh, Justus Uchenna Onu, Giles Newton‐Howes, Kim J. Masters, Yoav Kohn, Jacqueline Sin, Marie‐Hélène Goulet, Tonje Lossius Husum, Eleni Jelastopulu, Maria Bakola, Tim Opgenhaffen, Guru S. Gowda, Birhanie Mekuriaw, Kathleen De Cuyper, Eimear Muir‐Cochrane, Yana Canteloupe, Emer Diviney, Vincent S. Staggs, Melissa Petrakis

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MontréalInstitut Universitaire en Santé Mentale de Québec
FundersMonash University
KeywordsSnowball samplingOutreachMental healthInclusion (mineral)Best practiceTracking (education)Social mediaHealth carePsychologyMedicineNursingPublic relationsPolitical scienceSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.176
metaresearch head score (Gemma)0.165
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1760.165
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0060.004
Scholarly communication0.0060.007
Open science0.0030.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.371
GPT teacher head0.526
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainReporting
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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