Interactive acquisition and sharing: Understanding the dynamics of HIV/AIDS information networks
Bibliographic record
Abstract
Abstract HIV/AIDS information is an important resource for people affected by the disease, particularly information that they obtain from other people. Although existing studies reveal that people with HIV/AIDS (PHAs) rely extensively on personal relationships for HIV/AIDS information, they explain little about how this happens as a social process. To investigate how PHAs and their friends/family members acquire and share network‐mediated HIV/AIDS information, semistructured, in‐depth interviews were conducted in three rural regions of Canada. Interviews were carried out with 114 PHAs, their friends/family members, and health care and service providers. A network solicitation and chain‐referral recruitment procedure was used to delineate HIV/AIDS information networks for participants. Interview data were analyzed qualitatively and compared to Haythornthwaite's ( 1996 ) concepts of network‐mediated information processes and Talja and Hansen's ( 2006 ) collaborative information behavior framework. Findings revealed that participants obtained HIV/AIDS information from their networks through five interactive processes: joint seeking, tag‐team seeking, exposure, opportunity, and legitimation. The results of this study advance information behavior theory by pointing to the interactive character of information behavior and introducing new concepts to describe everyday life collaborative information behavior. This research also demonstrates the extensive interplay between health information exchange and the sharing of emotional support. The insights emanating from this study suggest that health information practice might benefit from a focus on program strategies such as building information network capacity, developing collaborative information retrieval systems and relationship‐building, in addition to the more traditional library‐related concerns of reference encounters, collections, and institutional Web sites.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".