Engaging Patients for Clinical Trials in Africa: Patient-Centered Approaches
Bibliographic record
Abstract
Clinical trials in oncology are an emergent field in sub-Saharan Africa. There is a long history of clinical trials in high-income countries (HICs), with increasing attempts to develop patient-centric approaches and to evaluate patient-centered outcomes. The challenge remains as to how these trends could be adopted in low-resource settings and adapted to best fit the different health ecosystems that coexist on the African continent. Models that evaluate patient-related outcomes and measures and that are used in HICs must be modified, adopted, and adapted to suit the diverse populations and the low-resource settings in most of the continent. Patient engagement in clinical trials in Africa must be well nuanced, and it demands innovation and application of models that consider established but tailored notions/principles of patient and community engagement and the unique sociocultural aspects of different populations. It also must be linked to strategies that aim to improve patient education, health literacy, and access to services and to encourage and protect patient autonomy.
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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 | 0.160 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".