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Record W2096148767 · doi:10.1002/jcop.21524

SOCIAL ROLE VALORIZATION IN COMMUNITY MENTAL HEALTH HOUSING: DOES IT CONTRIBUTE TO THE COMMUNITY INTEGRATION AND LIFE SATISFACTION OF PEOPLE WITH PSYCHIATRIC DISABILITIES?

2013· article· en· W2096148767 on OpenAlexaff
Tim Aubry, Robert J. Flynn, Barb Virley, Jaclynne Neri

Bibliographic record

VenueJournal of Community Psychology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCommunity integrationMental healthLife satisfactionPsychologySocial integrationPsychiatryClinical psychologyMedicineSocial psychologySociology

Abstract

fetched live from OpenAlex

Despite its importance as a theory in the development of programs for populations with disabilities, social role valorization (SRV) has received relatively little attention in community mental health research. We present findings of a study that examined the relationship of housing-related SRV to community integration and global life satisfaction of persons with psychiatric disabilities. The housing environments and associated supports of a group of 73 persons with psychiatric disabilities living in a mid-sized city were assessed using the PASSING rating system on the extent that their housing environments facilitated SRV. In addition, in-person interviews were conducted to determine the levels of physical integration, psychological integration, social integration, and life satisfaction of study participants. Results showed SRV contributing directly to all three types of community integration. Psychological integration was found to mediate the relationship between SRV and life satisfaction. Implications of the findings are discussed. © 2013 Wiley Periodicals, Inc.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.428
Teacher spread0.378 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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