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Record W4394771439 · doi:10.26689/jcnr.v8i3.6562

Development of Human Rights-Based Practices in Psychiatry FromPeople Living With Mental Health Problems Lived Experiences DuringInvoluntary Hospitalization or Treatment

2024· article· en· W4394771439 on OpenAlexaff
David Pelosse, Pierre Pariseau‐Legault

Bibliographic record

VenueJournal of Clinical and Nursing Research · 2024
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsCoercion (linguistics)Human rightsLived experienceInterpretative phenomenological analysisMental healthQualitative researchPhenomenology (philosophy)PsychologyCoding (social sciences)NursingMedicinePsychiatryPsychotherapistSociologyPolitical scienceSocial scienceEpistemology

Abstract

fetched live from OpenAlex

This article presents the research protocol of an interpretative phenomenological study that aims to understand the lived experiences of coercion and human rights-based practices in psychiatry from the perspectives of people living with mental health problems during involuntary hospitalization or treatment. This qualitative study used an interpretative phenomenological analysis design. In-depth, one-on-one interviews along with a socio-demographic questionnaire were conducted with approximately 10 participants. Data analysis was followed by an iterative and hermeneutic emergence coding process. By centering human rights-based practices on the lived experiences of people living with mental health problems who encountered coercion, this study highlighted the contributing and limiting factors to the recognition of human rights in nursing practices. This study also promoted the development of nursing knowledge and practices that can significantly contribute to an individual’s recovery process.

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)0.011
metaresearch head score (Gemma)0.015
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 score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.020
Scholarly communication0.0070.005
Open science0.0020.013
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.265
GPT teacher head0.587
Teacher spread0.323 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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