10: Capacity Building for Community Directed Research in East Africa (EA): 5 Year Microresearch (MR) Outcomes
Bibliographic record
Abstract
Sub-Saharan African countries have urged grassroots input to improve research capacity (WHO 2008) including in community directed research. In Uganda, Tanzania and Kenya, MR is building capacity to find local, sustainable, community solutions for local health problems. After five years of MR in EA, we report outcomes. MR training occurred during intensive two-week workshops (WS) where 20 to 30 health workers (HW) were introduced to principles of research, community engagement, knowledge translation, health policy. In small interdisciplinary teams (six to eight HW) self-identified community directed research outlines were created. Post WS, each team developed a full proposal supported by MR coaches (one EA, one NA) and submitted for international MR peer review. Following local ethics approval, successful projects were funded (up to $2,000). Projects were implemented, results reported and knowledge translated, including written report and extended abstract published in peer-reviewed PubMed journal. MR evaluation at five years consisted of review of WS participant and proposal data, standardized questions post each WS, input from attendees at two EA MR Forums held in March and November 2013. Between 2008 and mid-November 2013, 14 workshops were conducted at five EA training sites with 366 participants (43% female); 32% MD, 18% RN or Midwife, 50% other HW. By 2012, 27 projects approved for funding (74% in Uganda): seven completed, four published or accepted, 20 ongoing. Three projects helped change health policy/practice and four lead to career advancement. 37% focused on child health, 37% maternal health, 26% both. MR fostered gender equity in team PIs, funding success, EA coaches, MR local teachers. MR principles now in HW undergrad curriculum at two EA sites. Post WS, 90% participants rated WS positively; 20% noted MR changed culture of inquiry at work. Post MR 2013 Forums, an online MR network, MR alumni network and an EA MR site leaders consortium formed to grow MR. MR builds capacity for EA community directed interdisciplinary team research at modest cost. MR projects lead to local health care changes, enhance culture of inquiry. EA MR successes, with EA MR leadership will support growth beyond the five EA sites if resources become available.
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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 | 0.057 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".