Recruiting people with HIV to an online self-management support randomised controlled trial: barriers and facilitators
Why this work is in the frame
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Bibliographic record
Abstract
Background Recruitment of people to randomised trials of online interventions presents particular challenges and opportunities. The aim of this study was to evaluate factors associated with the recruitment of people with HIV (PWHIV) and their doctors to the HealthMap trial, a cluster randomised trial of an online self-management program. METHODS: Recruitment involved a three-step process. Study sites were recruited, followed by doctors caring for PWHIV at study sites and finally PWHIV. Data were collected from study sites, doctors and patient participants. Factors associated with site enrolment and patient participant recruitment were investigated using regression models. RESULTS: Thirteen study sites, 63 doctor participants and 728 patient participants were recruited to the study. Doctors having a prior relationship with the study investigators (odds ratio (OR) 13.3; 95% confidence interval (CI) 3.0, 58.7; P = 0.001) was positively associated with becoming a HealthMap site. Most patient participants successfully recruited to HealthMap (80%) had heard about the study from their HIV doctor. Patient enrolment was associated with the number of people with HIV receiving care at the site (β coefficient 0.10; 95% CI 0.04, 0.16; P = 0.004), but not with employing a clinic or research nurse to help recruit patients (β coefficient 55.9; 95% CI -2.55, 114.25; P = 0.06). CONCLUSION: Despite substantial investment in online promotion, a previous relationship with doctors was important for doctor recruitment, and doctors themselves were the most important source of patient recruitment to the HealthMap trial. Clinic-based recruitment strategies remain a critical component of trial recruitment, despite expanding opportunities to engage with online communities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it