P1-S6.05 Influence of social support networks on the HIV transmission risk behaviours of people living with HIV in Manitoba, Canada
Bibliographic record
Abstract
For people living with HIV/AIDS (PLWHA) positive social support networks (SSNs) can help improve quality of life, overall well-being, coping, and decrease mood disturbance, morbidity, mortality, sexual and substance associated HIV transmission risk factors. However, HIV diagnosis can cause a negative change in SSNs leading to social isolation (actual/perceived) and increase risk of HIV transmission behaviours. Having an effective strategy to encourage the development/maintenance of SSNs may have a positive effect upon the health outcomes and HIV transmission risk behaviours of PLWHA. Objective To describe the SSNs of Manitobans living with HIV/AIDS (MLHA) and determine the influence of SSNs on transmission risk behaviour. The relationship between independent variable (size and type of SSNs- positive/negative) and dependant variables (sexual risk behaviour, and alcohol, injection and non-injection drug use) was examined. Control variables included: age, gender, ethnicity, time since diagnosis, and sexual orientation. This data was collected in the Positive Prevention Study (PPS), a cross-sectional survey which included 135 MLHA aged 18 plus. The PPS assessed a broad list of transmission related determinants and only enrolled people if they were aware of their HIV diagnosis for at least 6 months, allowing for analysis of sustained positive behavioural changes. For this analysis SAS statistical software was used. Analysis of variance was done between the size and type (positive/negative) of SSNs and the chosen transmission risk behaviours; sexual behaviour, alcohol use, injection and non-injection drug use. Analysis of covariance was conducted with independent, dependent and control variables. Multiple regression analysis was run with independent and dependent variables to determine any relation. Level of social support achievable depends on one's attachment to those in their SSN and the role they play. It is not just the quantity of people but also the quality of relationships (eg, frequency, perceived support) that defines the success of SSNs. Not all SSNs are positive; some types may increase transmission risk behaviour. Only positive SSNs (regardless of size) are associated with avoidance of transmission risk behaviours. The results of this study help to assess the degree to which SSNs affect the sustainability of long-term secondary prevention measures, and thus inform groups offering services to MLHA with local scientific evidence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.002 |
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".