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Record W2257581898

Autonomy and co-management in mental health practice: the autonomous medication management device (AMM)

2013· article· en· W2257581898 on OpenAlexaboutno aff
Eduardo Passos, Analice de Lima Palombini, Rosana Teresa Onocko Campos, Sandro Rodrigues, Jorge Melo, Paula Milward Maggi, Cecília de Castro e Marques, Lívia Zanchet, Michele da Rocha Cervo, Bruno Ferrari Emerich

Bibliographic record

VenueLume (Universidade Federal do Rio Grande do Sul) · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyMental healthMedication therapy managementMedicinePsychologyBusinessNursingPsychiatryPolitical science
DOInot available

Abstract

fetched live from OpenAlex

O artigo aborda a articulação entre autonomia e cogestão nas práticas em saúde mental no Brasil, baseado em estudo multicêntrico. Tal estudo objetivou a elaboração do Guia Brasileiro da Gestão Autônoma da Medicação (Guia GAM-BR), com base na tradução e adaptação do Guia GAM desenvolvido no Quebec – instrumento dirigido a pessoas com transtornos mentais graves. Uma primeira versão do Guia GAM traduzida e adaptada ao contexto brasileiro foi utilizada em Grupos de Intervenção (GI) com usuários de serviços de saúde mental nos campos da pesquisa. A construção da versão final do Guia GAM brasileiro incluiu as modificações propostas pelos GI em cada campo, debatidas em reuniões multicêntricas com a participação de pesquisadores, trabalhadores e usuários integrantes dos GI. No curso da pesquisa, a estratégia GAM assumiu o desafio de propor-se como prática cogestiva, compatibilizando exercício da autonomia, direito e protagonismo dos usuários com o funcionamento e cultura organizacional das instituições de saúde mental.

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)0.008
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.313
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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