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Record W2599275576 · doi:10.1080/15487768.2017.1374219

A qualitative description of community service, business, and organization perspectives on mental illness and inclusion

2017· article· en· W2599275576 on OpenAlexafffundabout
Sean A. Kidd, Tyler Frederick, Lesley A. Tarasoff, Gursharan Virdee, Steve Lurie, Larry Davidson, David Morris, Kwame McKenzie

Bibliographic record

VenueAmerican Journal of Psychiatric Rehabilitation · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Mental Health AssociationOntario Tech UniversityUniversity of TorontoCentre for Addiction and Mental Health
FundersOntario Mental Health Foundation
KeywordsMental illnessInclusion (mineral)Qualitative researchStakeholderInclusion–exclusion principleStigma (botany)WarrantMental healthPsychologyPsychiatryPublic relationsSociologySocial psychologyPolitical scienceBusinessSocial science

Abstract

fetched live from OpenAlex

Although stigma associated with mental illness is pervasive, less is known about community stakeholder perspectives on inclusion and exclusion. This study provides a qualitative analysis of the mental illness–related experiences and perspectives of individuals who form much of the fabric of “community” for individuals with severe mental illness. In-depth interviews were conducted with a diverse group of 94 key community stakeholders in five neighborhoods in a large Canadian urban center. Qualitative analysis revealed a range of strategies that were used to foster inclusive spaces, the dilemmas that attended more severe forms of mental illness, and the importance of the meanings ascribed to mental illness in determining responses. Differences in response as a function of stakeholder group were also explored. There exist very promising resources and diverse perspectives on inclusion in urban communities that warrant further investigation given the intensive emphasis upon ‘community’ in policy and practice dialogues.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.438
Teacher spread0.356 · 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 teacher head, not a consensus.

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

Citations4
Published2017
Admission routes3
Has abstractyes

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