Recruiting people with HIV to an online self-management support randomised controlled trial: barriers and facilitators
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".