A multilevel model of HIV/AIDS information/help network development
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to describe the personal information and help networks of people with HIV/AIDS (PHAs) in rural Canada, and to present a research‐based model of how and why these networks developed. This model seeks to consider the roles of PHAs, their family members/friends and formal health systems in network formation. Design/methodology/approach In‐depth, semi‐structured interviews were conducted with 114 PHAs, their friends/family members (FFs) and formal caregivers in three rural regions of Canada. A network solicitation procedure elicited PHAs' HIV/AIDS information/help networks. Interviews were analyzed qualitatively, and network data were analyzed statistically. Documents describing health systems in each region were also analyzed. Analyses used social capital theory, supplemented by stress/coping and stigma management theories. Findings PHAs' HIV/AIDS‐related information/help networks emphasized linking and bonding social capital with minimal bridging social capital. This paper presents a model that explains how and why such networks developed. The model shows that networks grew from the actions of PHAs, their FFs and health systems. PHAs experienced considerable stress, which led them to develop information/help networks to cope with HIV/AIDS – both individually and collaboratively. Because of stigmatization, many PHAs disclosed their illness selectively, thus constraining the size and composition of their networks. Health system actors created network‐building opportunities for PHAs by providing them with care, referrals and support programs. Originality/value This study describes and explains an understudied type of information behavior: information/help network development at individual, group and institutional levels. As such, it illuminates the complex dynamics that made individual acts of interpersonal information acquisition and sharing possible.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it