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Record W185410771

Exploring the Health Issues of Hidden Homeless IV Drug users in a Mid-Sized Canadian Community.

2012· article· en· W185410771 on OpenAlexaffabout
Kimberley Dobson

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPopulationPsychologyAffect (linguistics)Coping (psychology)Social supportMedicineGerontologyEnvironmental healthPsychiatrySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the health issues that affect hidden homeless IV drug users, with respect to four determinants of health: physical environment, social environment, personal health practices and coping skills and health services. A comparison of hidden homeless IV drug users (IDUs) and non-injecting drug users (NIDUs) was conducted as a secondary analysis of data obtained in a needs assessment of the health and social services used by a hidden homeless population. The results suggest that IDUs have more health issues related to high risk behaviours, blood borne viral infections, stigmatization and lack of social support. Implications for practice include the need for a `housing first' approach to housing; creation of a drop-in centre; and education regarding homelessness and drug use to eliminate the stigmatization that exists towards this population. Further research with a larger sample of hidden homeless IDUs is warranted.

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)0.002
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 score0.028
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.147
GPT teacher head0.354
Teacher spread0.208 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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