Understanding HIV transmission risk behavior among HIV-infected South Africans receiving antiretroviral therapy: An Information—Motivation—Behavioral Skills Model analysis.
Bibliographic record
Abstract
OBJECTIVE: The current study applied the Information-Motivation-Behavioral Skills (IMB) model (Fisher & Fisher, 1992; Fisher & Fisher, 1993) to identify factors associated with human immunodeficiency virus (HIV) transmission risk behavior among HIV-infected South Africans receiving antiretroviral therapy (ART), a population of considerable significance for curtailing, or maintaining, South Africa's generalized HIV epidemic. METHODS: HIV prevention information, HIV prevention motivation, HIV prevention behavioral skills, and HIV transmission risk behavior were assessed in a sample of 1,388 South Africans infected with HIV and receiving ART in 16 clinics in KwaZulu-Natal, South Africa. RESULTS: Findings confirmed the assumptions of the IMB model and demonstrated that HIV prevention information and HIV prevention motivation work through HIV prevention behavioral skills to affect HIV transmission risk behavior in this population. Subanalyses confirmed these relationships for HIV transmission risk behavior overall and for HIV transmission risk behavior with partners perceived to be HIV-negative or HIV-status unknown. A consistent pattern of gender differences showed that for men, HIV prevention information and HIV prevention motivation may have direct links with HIV preventive behavior, whereas for women, the effect of HIV prevention motivation works through HIV prevention behavioral skills to affect HIV preventive behavior. CONCLUSION: These IMB model-based findings suggest directions for HIV prevention interventions with South African men and women living with HIV and on ART as an important component of overall strategies to contain South Africa's generalized HIV epidemic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".