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Record W3088484004 · doi:10.1177/1049732320954396

Harm Reduction for Women in Treatment for Alcohol Use Problems: Exploring the Impact of Dominant Addiction Discourse

2020· article· en· W3088484004 on OpenAlexafffundabout
Catrina Brown, Sherry H. Stewart

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarm reductionAbstinenceAmbivalenceHarmAddictionPsychiatryService providerAlcoholNarrativeMedicinePsychologyService (business)Clinical psychologyNursingSocial psychologyPublic healthBusiness

Abstract

fetched live from OpenAlex

The objectives of this study were to profile the landscape of women's alcohol use programs in Canada. We explored service users' and providers' beliefs about alcohol use problems and how this affected treatment choices for alcohol use problems. Data were collected through standardized measures alongside in-depth semi-structured narrative interviews in six women's alcohol treatment sites in Canada. Findings demonstrated that service users and service providers often supported an abstinence choice and were ambivalent about the viability of controlled or managed use in both abstinence- and harm reduction-based programs. Findings showed that women service users in this study had significant rates of trauma and depression which were associated with their alcohol use; the majority still adopted dominant alcohol addiction discourse which emphasizes the need for abstinence. We offer a number of recommendations to improve the viability of harm reduction for alcohol use in women's treatment programs.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.075
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.729
GPT teacher head0.615
Teacher spread0.114 · 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

Citations20
Published2020
Admission routes3
Has abstractyes

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