Bibliographic record
Abstract
Clinical trials conducted in low-resource settings have unique challenges associated with their conduct. This is mainly attributed to the power and resource discrepancy between actors in the clinical trial. This thesis provides an in-depth look at clinical trials in low-resource settings and the effects of the resource discrepancy on the actors. It aims to answer what the ethical challenges are when conducting research in low-resource settings and the subsequent implications for research design. It focuses on capturing both the experience of caregivers of pediatric participants and the frontline researchers in a malaria vaccine clinical trial. Through exploring these two stories and bridging the relational with the formal, it provides a novel approach to address the challenges with research in low-resource settings. This approach employs the lens of complexity theory to evaluate the outcome of two systems, a human community and a clinical trial, merging. I will begin by outlining a general introduction of clinical trials in low-resource settings and the case study of a pediatric malaria vaccine clinical trial, here I detail the need to generate a vaccine against malaria and outline why such research should take place. This situates the reasons for the study and provides familiarity with the contextual reality. Then I will move into detailing the caregiver experiences, researcher experiences, and the application of complexity theory to bridge together the different experiences. This thesis is a result of qualitative data gathered from 78 interviews with caregivers of pediatric participants and 11 interviews with researchers involved on the frontline of a pediatric malaria vaccine clinical trial. The final part of the thesis is a theoretical reflection that explores the realities faced by researchers and argues for an approach that embraces the non-linearity of research taking place in human communities. Here I identify the challenges associated with choice and structural inequity, the conflict between beneficence and autonomy, and being a frontline researcher in low-resource settings.
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 | 0.163 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.077 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".