A multilevel model of HIV/AIDS information/help network development
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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.
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".