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Record W2170003417

BREAKING THE SILENCE: MESSAGING AROUND HIV PREVENTION FOR SOUTH ASIAN WOMEN IN TORONTO

2010· article· en· W2170003417 on OpenAlexaboutno aff
Shilpa Raju

Bibliographic record

VenueSummit (Simon Fraser University) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
Fundersnot available
KeywordsSilenceHuman immunodeficiency virus (HIV)Gender studiesMedicineInternet privacyPsychologyGerontologyFamily medicineComputer scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the number of HIV infections among South Asian women in Toronto contracted through heterosexual transmission has been increasing, potentially attributable to a number of factors and vulnerabilities. This situation will be examined including a review of trends since 1980 and an in-depth analysis of contributing factors. Regarding a general increase in infections among women in Canada, it appears that greater efforts are required to improve awareness around HIV prevention, and one widely used method has been public messaging through poster campaigns. As a result, this paper proposes that a poster campaign, which has previously not been explored or launched targeting this community in particular (South Asian women in Toronto) would be useful. It suggests elements that should be included in a campaign, how the issue should be addressed, what the messaging should look like, and broader goals of such a campaign for the community.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.654
Threshold uncertainty score0.688

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.342
Teacher spread0.304 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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