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Record W2072624292 · doi:10.1080/01612840802624467

Exploring Differences Between Community-Based Women and Men with a History of Mental Illness

2009· article· en· W2072624292 on OpenAlexaffabout
Cheryl Forchuk, Elsabeth Jensen, Rick Csiernik, Catherine Ward‐Griffin, Susan L. Ray, Phyllis Montgomery, Linda Wan

Bibliographic record

VenueIssues in Mental Health Nursing · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsThe King's UniversityLaurentian UniversityYork UniversityLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMental healthMental illnessPsychologyPsychiatryAllianceGerontologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Relatively little is understood concerning the role of gender in persons with a history of mental illness residing in the community. This paper aims to explore gender's effect using data from the Community Research University Alliance project entitled, Mental Health and Housing. The primary five-year longitudinal study examined housing situations for psychiatric consumer/survivors in a mid-size, central Canadian region in an effort to improve the number and quality of appropriate housing situations. Data from 887 subjects in the original research underwent secondary analysis with particular relevance to differences between gender and indicators of health status including psychiatric history, levels of functioning, personal strengths and resources, and illness severity. Results of the secondary analysis found male and female differences that corroborated previous research. More women are housed than men, more women with mental illness were coupled than men, men have fewer social supports, and men have more substance abuse issues than women. These findings suggest health services within the community must consider these sex differences if they wish to properly assist Canadian individuals diagnosed with mental illnesses.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.142
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.435
Teacher spread0.272 · 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.

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

Citations5
Published2009
Admission routes2
Has abstractyes

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