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Record W3107390393 · doi:10.7870/cjcmh-2020-018

Adherence to Treatment in First Episode Psychosis: Acceptance, Refusal, or Ambivalent Process?

2020· article· en· W3107390393 on OpenAlexaffvenue
Laurence Artaud, Amal Abdel‐Baki, Marie Nolin, Cécile Rousseau

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsAmbivalenceContext (archaeology)EmpowermentPsychologyIdentity (music)Focus groupConstruct (python library)Qualitative researchPsychology of selfPsychotherapistSocial psychologyPsychosisClinical psychologyDevelopmental psychologyPsychiatrySociologyPolitical scienceAesthetics

Abstract

fetched live from OpenAlex

Drawing from the perspectives of 18 youth (characterized as adherent, ambivalent, nonadherent), 13 relatives and 8 clinicians, this qualitative study, using semi-structured individual interviews and a focus group, explores why individuals suffering from early psychosis accept or refuse treatment. Identity and relationships, understanding of the diagnosis and significance given to treatment are key themes. Varying degrees of ambivalence and nonadherence can be seen as normal stages of a process to rebuild their sense of self and construct their identity. A trusting relationship may facilitate gradual resolution of ambivalence, promoting the individuals’ sense of ownership and empowerment in the context of treatment.

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)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.405
Teacher spread0.285 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

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