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Record W63049315 · doi:10.22329/csw.v8i1.5743

De “myth” ifying Mental Health – Findings from a Community University Research Alliance (CURA)

2019· article· en· W63049315 on OpenAlexaffvenueabout
Rick Csiernik, Cheryl Forchuk, Mark Speechley, Catherine Ward‐Griffin

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsMental healthAllianceGeneral partnershipMental illnessPsychiatryPsychologyMedicineGerontologyPolitical science

Abstract

fetched live from OpenAlex

Many myths exist regarding mental illness and those with mental health issues. Under the auspices of a Community-University Research Alliance on Housing and Mental Health, a partnership between academics, community health and social service agencies and representatives of consumer-survivor groups, fourteen consumer-survivor and eight family member focus groups were held throughout Southwestern Ontario. Individual interviews were also conducted with 150 male and 150 female community-based mental health system consumer-survivors living in a variety of housing environments in London, Ontario. The findings dispute beliefs around four myths: that people with mental health problems are a homogenous population, which was highlighted by significant differences between men and women in frequency and length of psychiatric hospitalizations, primary diagnosis, problem severity, psychoactive drug use and sexual abuse, are unemployed because they are uneducated, are violent and dangerous and thus spend extended periods of time incarcerated and are unsupported by their families which then leads to housing problems. Challenging these and other equally erroneous myths is essential in responding to the oppression faced by mental-health consumer-survivors and in developing a national strategy for mental health.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.204
GPT teacher head0.517
Teacher spread0.313 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes3
Has abstractyes

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