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Record W2948058174 · doi:10.1080/14999013.2019.1619000

Multiple Stakeholders’ Perspectives of Involuntary Treatment Orders: A Meta-synthesis of the Qualitative Evidence toward an Exploratory Model

2019· article· en· W2948058174 on OpenAlexafffund
Marie‐Hélène Goulet, Pierre Pariseau‐Legault, Cindy Côté, Alana Klein, Anne G. Crocker

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité de MontréalMcGill UniversityUniversité du Québec en OutaouaisInstitut national de psychiatrie légale Philippe-Pinel
FundersCanadian Institutes of Health Research
KeywordsLeverage (statistics)Qualitative researchContext (archaeology)Exploratory researchMental healthPsychologyFocus groupMedicinePsychotherapistComputer scienceBusinessSociology

Abstract

fetched live from OpenAlex

Involuntary treatment orders (ITOs) represent coercive leverage for treatment adherence against the will of individuals incapable of providing consent. ITOs have failed to demonstrate benefits in quantitative studies, but little attention has been paid the growing body of qualitative evidence on ITOs. The current study is an interpretative meta-synthesis designed to integrate qualitative evidence and enhance our understanding of stakeholders’ perspectives (service users, relatives, professionals, psychiatrists) of ITOs in the context of mental health care. Forty-four studies met the following inclusion criteria, peer-reviewed empirical qualitative studies, and focus on perspectives and experiences of ITOs in a mental health context. Themes resulting from the analysis are: an ITO as leverage to manage compliance and risk; legal concerns; learning to play the game; building a therapeutic relationship in a coercive context; positive and negative impacts of ITOs; family involvement; and discharge. Based on these themes, an exploratory model of ITOs is proposed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.396
GPT teacher head0.486
Teacher spread0.090 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
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

Citations23
Published2019
Admission routes2
Has abstractyes

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