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Record W1585901907 · doi:10.1186/1477-7517-3-30

Harm reduction services for British Columbia's First Nation population: a qualitative inquiry into opportunities and barriers for injection drug users

2006· article· en· W1585901907 on OpenAlexaffabout
Dennis Wardman, Darryl Quantz

Bibliographic record

VenueHarm Reduction Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsHarm reductionHealth psychologySocial policyQualitative researchPopulationPublic healthHarmSocial workDrugPublic relationsSociologyMedicinePolitical scienceNursingPsychologySocial psychologyEnvironmental healthSocial sciencePharmacologyLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Aboriginal injection drug users are the fastest growing group of new Human Immunodeficiency Virus cases in Canada. However, there remains a lack of comprehensive harm reduction services available to First Nation persons, particularly for First Nation people dwelling in rural and reserve communities. This paper reports findings from an exploratory study of current harm reduction practices in First Nation communities. The purpose of this study was to provide an overview of the availability and content of current harm reduction practices, as well as to identify barriers and opportunities for implementing these services in First Nation communities. METHODS: Key informant interviews were conducted with 13 addictions service providers from the province of British Columbia, Canada. RESULTS: Participants identified barriers to these services such as community size and limited service infrastructure, lack of financial resources, attitudes towards harm reduction services and cultural differences. CONCLUSION: It was recommended that community education efforts be directed broadly within the community before establishing harm reduction services and that the readiness of communities be assessed.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.091
GPT teacher head0.364
Teacher spread0.274 · 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

Citations23
Published2006
Admission routes2
Has abstractyes

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