Establishing an online HIV peer helping programme: A review of process challenges and lessons learned
Bibliographic record
Abstract
Background: Online peer support can be a valuable approach to helping people living with HIV, especially in regions with large rural populations and relatively centralised HIV services. Design: This paper focuses on a community -university partnership aimed at developing an online peer support programme in the Canadian province of Newfoundland and Labrador. Setting: Team members included community representatives and people living with HIV from the AIDS Committee of Newfoundland and Labrador (ACNL) as well as academic researchers. Objectives: Goals and objectives of the programme included reaching disconnected people living with HIV, reducing isolation among people living with HIV and connecting people living with HIV with support, education and professional resources. Method and Results: Through a process orientated and iterative decision-making approach, the team established the website, peer helping training curriculum, a recruitment plan as well as other core considerations. The current paper emphasises several process challenges and lessons learned from the development stage of the online support programme. Conclusion: It is hoped that this information will assist others in avoiding or overcoming similar process challenges arising during such work.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".