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Perceptions of health and health service utilization among homeless and housed psychiatric consumer/survivors

2008· article· en· W2144858869 on OpenAlexaffabout
Cheryl Forchuk, Stephanie A. Brown, Ruth Schofield, Elsabeth Jensen

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork UniversityMcMaster UniversityWestern University
Fundersnot available
KeywordsMental healthAllianceMedicineHealth careGerontologyService (business)PsychiatryHealth servicesPerceptionPopulationPsychologyEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Homelessness has a direct impact on health. Homeless individuals report several barriers to accessing health care. Although research exists regarding the utilization of health services for homeless and housed psychiatric consumer/survivors, few studies have compared the perceived health and service utilization of these two groups. The objective of this study was to determine whether or not differences exist between the utilization of health services and the perceptions of health of homeless and housed psychiatric consumer/survivors in London, Ontario, Canada. It was hypothesized that differences would exist between homeless and housed psychiatric consumer/survivors on all health-related variables examined. A secondary analysis of quantitative data was conducted in a Community-University Research Alliance on Mental Health and Housing project funded by the Social Sciences and Humanities Research Council of Canada. Key findings include significant differences in the characteristics of each population, the use of health services and their perceptions of health. Implications for practice and policy are discussed.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.074
GPT teacher head0.430
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 source (direct Gemma or distilled Codex), 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

Citations27
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Psychiatric and Mental Health NursingSame topicHomelessness and Social IssuesFrench-language works237,207