PA-81 Bridging the gap: aligning research efforts with disparities in burden of disease – experiences of early-career researcher conducting investigator-initiated trial in a low-income country
Bibliographic record
Abstract
Background Low-income countries bear 90% of the worldwide burden of disease yet there is underrepresentation of research addressing priority issues for low-income countries. Lack of research skills exacerbate the problem. While calls support locally driven research, investigators initiating RCTs in low-income countries encounter barriers, preventing RCT execution. We use our investigator self-initiated trial to provide some of our experiences, in executing a trial in a low-income country. Methods We are conducting a large RCT to determine the effect of text messaging plus motivational interviewing on sustaining breastfeeding, among 275 women living with HIV. Results We first assessed the feasibility of the large trial. We submitted multiple grant applications for the pilot trial and secured enough funds within three years. Some awarded funds were returned due to grant timeframe conditions. The pilot trial capitalized on existing research infrastructure. In 2020, we secured the EDCTP2 grant for the large trial. Lack of infrastructural support negatively affected the budget. The pilot trial was exempted from ethics fee. The large trial was approved by ethics before the EDCTP action period, due to tight funding timeframe. We secured funds elsewhere for ethics fee. Each study was approved within three months. The Western Cape Government, Department of Health (WCDH) has a National Health Research Database assisting researchers with applications submission for review by the Provincial Health Research Committee granting access to provincial healthcare facilities. WCDH approved each study within six months. We recruited from a healthcare facility, serving a small pool of our target population which prolonged pilot trial recruitment. We use Research Electronic Data Capture at no cost. Conclusion Enabling environments improve efficiency of trial execution. Leveraging on existing research infrastructure optimize use of available resources. Small research grants should consider flexible funding timeframes. Collaboration with stakeholders in routine healthcare facilitates facility access for research.
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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 | 0.145 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".