Impact of community engagement and social support on the outcomes of HIV-related meningitis clinical trials in a resource-limited setting
Bibliographic record
Abstract
BACKGROUND: Clinical trials remain the cornerstone of improving outcomes for HIV-infected individuals with cryptococcal meningitis. Community engagement aims at involving participants and their advocates as partners in research rather than merely trial subjects. Community engagement can help to build trust in communities where these trials are conducted and ensure lasting mutually beneficial relationships between researchers and the community. Similarly, different studies have reported the positive effects of social support on patient's outcomes. We aimed to describe our approach to community engagement in Uganda while highlighting the benefits of community engagement and social support in clinical trials managing patients co-infected with HIV and cryptococcal meningitis. METHODS: We carried out community engagement using home visits, health talks, posters, music and drama. In addition, social support was given through study staff individually contributing to provide funds for participants' food, wheel chairs, imaging studies, adult diapers, and other extra investigations or drugs that were not covered by the study budget or protocol. The benefits of this community engagement and social support were assessed during two multi-site, randomized cryptococcal meningitis clinical trials in Uganda. RESULTS: We screened 1739 HIV-infected adults and enrolled 934 with cryptococcal meningitis into the COAT and ASTRO-CM trials during the period October 2010 to July 2017. Lumbar puncture refusal rates decreased from 31% in 2010 to less than 1% in 2017. In our opinion, community engagement and social support played an important role in improving: drug adherence, acceptance of lumbar punctures, data completeness, rate of screening/referrals, reduction of missed visits, and loss to follow-up. CONCLUSIONS: Community engagement and social support are important aspects of clinical research and should be incorporated into clinical trial design and conduct. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01075152 and NCT01802385.
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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 | 0.129 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".