Meeting health information needs of people with HIV/AIDS: sources and means of collaboration
Bibliographic record
Abstract
BACKGROUND: Internet-based applications, in particular those that allow communication, have great potential to meet information needs. Limited research has indicated that people with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS; PHAs) use these technologies, but it has not yet been examined how resources are used collaboratively and in conjunction with offline sources. OBJECTIVES: The purpose of this study was to determine in what ways PHAs collaborate to meet treatment information needs and what role Internet-based computer-mediated communication (CMC) played in meeting this goal. METHODS: This exploratory study was implemented using surveys and focus groups with 23 participants in Toronto, Canada. The purposive sample included men and women. RESULTS: A variety of both off- and online resources were used to learn about HIV/AIDS treatment information, including web-based and print. All participants were communicating with others, primarily in person, and most desired anecdotal treatment information. However, few reported using CMC to accomplish this goal. Harris and Dewdney's Principles of Information Seeking was used to frame the findings. CONCLUSIONS: Despite technical proficiency with CMC, few participants in this study reported use of this communication tool. Information professionals need to ensure access to HIV health information including those in remote areas who have fewer resources.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".