Conducting clinical research in a resource-constrained setting: lessons from a longitudinal cohort study in The Gambia
Bibliographic record
Abstract
Clinical research conducted to Good Clinical Practice (GCP) standards is increasingly being undertaken in resource-constrained low-income and middle-income countries (LMICs) settings. This presents unique challenges that differ from those faced in high-income country (HIC) contexts, due to a dearth of infrastructure and unique socio-cultural contexts. Field experiences by research teams working in these LMIC contexts are thus critical to advancing knowledge on successful research conduct in these settings. The Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine has operated in The Gambia, a resource-constrained LMIC for over 70 years and has developed numerous research support platforms and systems. The unit was the lead clinical collaborator in a recently completed Expanded Program on Immunization Consortium (EPIC) study, involving a multicountry collaboration across five countries including the USA, Canada, Belgium, Papua New Guinea and The Gambia. The EPIC study recruited and completed follow-up of 720 newborn infants over 2 years. In this paper, we provide in-depth field experience covering challenges faced by the Gambian EPIC team in the conduct of this study. We also detail some reflections on these challenges. Our findings are relevant to the international research community as they highlight practical day-to-day challenges in conducting GCP standard clinical research in resource-constrained LMIC contexts. They also provide insights on how study processes can be adapted early during research planning to mitigate challenges.
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 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 | 0.025 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".