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Record W2769168232 · doi:10.1590/0102-311x00166216

Narrativas de usuários de saúde mental em uma experiência de gestão autônoma de medicação

2017· article· pt· W2769168232 on OpenAlexaboutno aff
Laura Lamas Martins Gonçalves, Rosana Teresa Onocko Campos

Bibliographic record

VenueCadernos de Saúde Pública · 2017
Typearticle
Languagept
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialEmpowermentMental healthIntervention (counseling)Psychological interventionPsychologyHumanitiesMedicineNursingPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Numerous studies have highlighted the tendency to chronicity of treatments centered on the use of medications. This study was conducted in a large Brazilian city with users of Centers for Psychosocial Care (CAPS), with the aim of evaluating the effects of experimenting with the personal Guide for Autonomous Management of Medication (GAM) and the users' relationship to their treatments and participation. The instrument, created in Canada and translated and adapted to Brazil, was tested in intervention groups with users of CAPS with severe mental disorders and a history of political participation in the field of health. Focus groups and in-depth interviews were performed. The transcriptions were transformed into narratives, and four analytical categories were defined: subjects with radical experiences of suffering; experiences with medication; users' rights; and participation and political activism. In testing GAM, users expressed a tension between reproduction of illness-centered identity and the legitimate uniqueness of their personal experiences. They showed greater knowledge of the medications they were taking, began to recognize their own expertise in their use of medications, and some sought adjustments to their treatment. They reported the need for support in claiming their rights and strengthening their participation as mental health activists. In conclusion, the Brazilian version of the GAM guide has the potential to contribute to users' empowerment, thus allowing dialogue between the results in mental health care in Brazil and the international scenario.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.218
GPT teacher head0.445
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2017
Admission routes1
Has abstractyes

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