MicroResearch: Finding sustainable local health solutions in East Africa through small local research studies
Bibliographic record
Abstract
BACKGROUND: Sub-Saharan African countries have urged grassroots input to improve research capacity. In East Africa, MicroResearch is fostering local ability to find sustainable solutions for community health problems. At 5years, the following reports its progress. METHODS: The MicroResearch program had three integrated components: (1) 2-week training workshops; (2) small proposal development with international peer review followed by project funding, implementation, knowledge translation; (3) coaching from experienced researchers. Evaluation included standardized questions after completion of the workshops, 2013 online survey of recent workshop participants and discussions at two East Africa MicroResearch Forums in 2013. RESULTS: Between 2008 and 2013, 15 workshops were conducted at 5 East Africa sites with 391 participants. Of the 29 projects funded by MicroResearch, 7 have been completed; of which 6 led to changes in local health policy/practice. MicroResearch training stimulated 13 other funded research projects; of which 8 were external to MicroResearch. Over 90% of participants rated the workshops as excellent with 20% spontaneously noting that MicroResearch changed how they worked. The survey highlighted three local research needs: mentors, skills and funding - each addressed by MicroResearch. On-line MicroResearch and alumni networks, two knowledge translation partnerships and an East Africa Leaders Consortium arose from the MicroResearch Forums. CONCLUSION: MicroResearch helped build local capacity for community-directed interdisciplinary health 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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".