“A lot of people didn't have a chance to support us because we never told them” Stigma management, information poverty and HIV/AIDS information/help networks
Bibliographic record
Abstract
Abstract Because of fears of stigmatization, people with HIV/AIDS (PHAs) may avoid health care and refuse illness‐related information and support. However, HIV/AIDS‐related information, especially that which is provided by other people, has also been shown to a vital resource for PHAs and their loved ones. This research examines the role of stigmatization in PHAs' and their friends/family members' efforts to establish personal networks for HIV/AIDS‐related information and help ("information/help networks"). To investigate this question, I draw upon Goffman's () stigma management theory and Chatman's () theory of information poverty. Semi‐structured, in‐depth interviews were conducted with 114 PHAs, their friends/family members, health care and service providers in three rural regions of Canada. Results revealed that the majority of PHAs and friends/family members had relatively small networks for HIV/AIDS information/help. For many participants, the challenges of living with HIV/AIDS led to changes in their personal networks, and stigmatization playing a significant part in such changes. Participants developed information/help networks in a manner consistent with stigma management theory in their decisions to disclose selectively to others, to avoid the topic in conversation, to obtain information/help at a distance or to acquire information/help without disclosing their HIV status. However, in contrast to Chatman's theory, participants did not wholly avoid information from interpersonal sources nor believe that no one was available to help them. These findings suggest that information behaviour theory may need to evolve in order to account for the complexity of self‐protective behaviour.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".