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Record W4299687080

[Evaluation of a citizenship-oriented intervention: The Citizens' Project of the University of Recovery].

2017· article· en· W4299687080 on OpenAlexaffabout
Jean‐François Pelletier, Denis Pouliot-Morneau, Janie Houle, Julie Bordeleau, Sébastien Laroche, Michael Rowe

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité du Québec à MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsCitizenshipGeneral partnershipMental healthPublic relationsPromotion (chess)Health careFocus groupActive citizenshipPublic healthNursingPolitical sciencePsychologyMedicineSociologyPoliticsPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objectives The Global Model of Public Mental Health is "global" not only in the sense of having an international perspective, but in regarding service users as actors at all levels of public mental health exerting collective and organized influence on the social determinants of health, in addition to being recipients of care. Having access to appropriate health and mental health care when needed is a fundamental human right. Having a say over the manner in which care is provided, including partnership in decision making in care planning and ongoing care, has gained increasing support among recipients and providers of care. Over the past few decades in the Canadian province of Quebec, patient participation and partnership in decision-making has been promoted through successive Mental Health Action Plans (MHAP) and other policies. In these documents, participation and partnership are associated with the exercise of citizenship and the promotion of service users' rights, including the rights to participate in one's own care. In this article, using the case example of a citizenship-oriented intervention, namely the Projet citoyen, we discuss the results to a new measure of citizenship, which was developed from a service users' perspective.Methods Employing a mixed methods approach, two types of data were collected from users of mental health care. Quantitative data were generated from administration of a 23-item measure of citizenship with service users in the province of Quebec (N=802), and qualitative data were collected from four focus groups with another sample of 18 service users. They were presented with results from the administration of the measure, and asked to comment on them in regard to their own experience of citizenship.Results Among the five dimensions of the measure of citizenship, participants scored lowest on the 'involvement in the community' dimension, and higher on the other dimensions of 'basic needs,' 'respect by others,' 'self-determination,' and 'access to services.' In focus groups, participants said that there is still prejudice in society and discrimination towards people with mental illnesses that limit their right to participate in public debate and mental health programming. Public health interventions at this level may help to change attitudes and social representations, as they are inclusive of persons with lived experience of mental illness. Public discussion of citizenship issues in relation to mental health also represent an opportunity for participants to confront existing problems, as a first step toward collective action.Conclusion People's lived experience of regaining a sense of citizenship and of belonging to their local neighborhoods and communities, including the scientific micro-community, can help to foster an evolution of public health from disease management to health promotion and community inclusion. More research is needed to compare the sense of citizenship to the rest of the population and to see if specific interventions can have an enduring impact (e.g.: pre/post design).

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)0.026
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 score0.062
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0080.005
Scholarly communication0.0020.002
Open science0.0030.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.347
GPT teacher head0.406
Teacher spread0.059 · 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".

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Citations5
Published2017
Admission routes2
Has abstractyes

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