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Record W2505327082 · doi:10.1177/2158244016663799

Engaging Black Communities to Address HIV

2016· article· en· W2505327082 on OpenAlexaffabout
Shamara Baidoobonso, Winston Husbands, Clemon George, Tola Mbulaheni, Arfan R. Afzal

Bibliographic record

VenueSAGE Open · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsOntario Tech UniversityAIDS Committee of TorontoWestern University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)AppealSocial marketingPopulationAdvertisingPsychologyGeographyMedicineEnvironmental healthPolitical sciencePublic relationsFamily medicineBusiness

Abstract

fetched live from OpenAlex

Black people comprise 4.3% of Ontario’s population but 19% of HIV-positive people in the province. The “Keep it alive!” social marketing campaign was developed to promote HIV prevention and raise awareness about HIV among Ontario’s Black communities. This article evaluates the campaign’s reception. A convenience sample of 243 Black people completed a cross-sectional self-administered survey in three cities. We assessed the campaign’s reception based on survey responses about campaign exposure, appeal, and importance, and whether the campaign raised awareness. Our results show that reception was more favorable among participants who tested for HIV previously, discussed the campaign with others, demonstrated a superior knowledge of HIV, visited the campaign website, were of Caribbean or African background, and were male. In addition, reception varied by city and according to participants’ language (English or French). These results may inform future campaigns, although how campaigns are received may reflect issues related to their implementation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.254
GPT teacher head0.494
Teacher spread0.239 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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