Challenges with participant reimbursement: experiences from a post-trial access study
Bibliographic record
Abstract
Reimbursement of trial participants remains a frequently debated issue, with specific guidance lacking. Trials combining post-trial access and implementation science may necessitate new strategies and models. CAPRISA 008, a post-trial access study testing the feasibility of using family planning services to rollout a prelicensure HIV prevention intervention, tried to balance the real-life scenario of no reimbursement for attendance at public sector clinics with that of a trial including some visits that focused on research procedures and others that focused on standard of care procedures. A reduced reimbursement was offered for 'standard of care' visits, meant primarily to cover transport costs to and from the clinic only. This impacted negatively on accrual, retention and participant morale, primarily due to the protracted delay in regulatory approval, during which time, the costs of living, including travel costs had increased. Relevant guidelines were reviewed and institutional policy was updated to incorporate the South African National Health Research Ethics Committee guidelines on reimbursement (taking into account participant time, travel and inconvenience). The reimbursement amount for 'standard of care' visits was increased accordingly. The question remains whether a trial that combines post-trial access with implementation science, with clear benefits for the participants and the provision of above standard medical care, should have reimbursement rates that approach those of a proof-of-concept trial, for 'standard of care' visits.
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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 | 0.299 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".