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Record W7133012549

Women's Stories/Women's Lives: Creating Safer Crack Kits

2009· article· en· W7133012549 on OpenAlexfundaboutno aff
Vicky Bungay, Joy L. Johnson, Susan C. Boyd, Leslie Malchy, Jane A. Buxton, Jodi Loudfoot

Bibliographic record

VenueTSpace · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersHealth Canada
KeywordsSAFERQualitative researchNeighbourhood (mathematics)Relevance (law)HarmFocus groupService providerWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

In 2004, a research team comprising researchers and service providers launched the Safer Crack Use, Outreach, Research and Education (SCORE) project, aimed at developing a better understanding of the harms associated with crack cocaine smoking and determining the feasibility of specific harm reduction strategies to reduce the likelihood of harms in an inner-city neighbourhood in Canada. The project included several activities, and the ‘women-centred’ activity of constructing harm-reduction kits is the emphasis of this paper. The data for this study are derived from the field notes taken during kit-making sessions with 200 women and from qualitative interviews with the group facilitators. A salient theme of the analysis was the tremendous support that was afforded to women engaging in this activity. Three sub-themes were also identified: (a) creating a safe space, (b) sharing information, and (c) building community. Women-centred activities are an effective means of creating a supportive environment for women and to learn about women’s perspectives concerning the relevance of these activities in their daily lives.

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)0.004
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.009
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.077
GPT teacher head0.413
Teacher spread0.336 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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