Talking About, Knowing About HIV/AIDS in Canada: A Rural-Urban Comparison
Bibliographic record
Abstract
PURPOSE: To explore information exchange about HIV/AIDS among people living in rural and urban communities and to assess the value of social capital theory, as well as demographic factors, in predicting community members' knowledge of HIV/AIDS and their likelihood of having talked about the disease. METHOD: A random-digit dial telephone survey was conducted in 3 rural regions and matched urban communities in Canada during 2006 and 2007. A total of 1,919 respondents (response rate: 22.2%) answered questions about their knowledge of and attitudes toward HIV/AIDS, their social networks, whether they were personally acquainted with a person with HIV/AIDS (PHA), and whether they had ever talked to anyone about HIV/AIDS. FINDINGS: Rurality was a significant predictor of HIV/AIDS knowledge and discussion. Even after controlling for factors such as age and level of education, respondents living in rural regions were less knowledgeable about HIV/AIDS and were less likely to have spoken with others about the disease. Social capital theory was not as strongly predictive as expected, although people with more bridging ties in their social networks were more likely to have discussed the disease, as were those who knew a PHA personally. CONCLUSION: Rural-dwelling Canadians are less likely than their urban counterparts to be knowledgeable about HIV/AIDS or to talk about it, confirming reports by PHAs that rural communities tend to be silent about the disease. The findings support policy recommendations for HIV education programs in rural areas that encourage discussion about the disease and personal contact with PHAs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".