9: Assessing Healthcare Needs and Research Barriers for Community Focused Interdisciplinary Health Research Capacity Building Using a Microresearch Model in East Africa
Bibliographic record
Abstract
With barely 3% of health care workers (HCW) and over 25% of the global health burden, Sub-Sahara African nations saw an urgent need to build capacity in community focused research in 2008 (WHO Bamako ‘Call for Action’). Borrowing from principles of microfinance, MicroResearch (MR) provides training, coaching and small grants for community focused interdisciplinary research (CFIR) and knowledge translation. To assess if MicroResearch addresses the local need for CFIR in East Africa (EA). We used targeted on-line surveys to assess health challenges and barriers for EA HCW researchers. In phase 1, questionnaires to assess health care challenges were sent to senior researchers (deans, department heads) and junior HCW engaged in CFIR. In phase 2, we assessed an on-line focus group of community researchers from five academic sites in EA using qualitative analyse of responses to prompted questions. In phase 1, 68 questionnaires were distributed, and 40 (59%) responded, 17 from senior and 23 junior HCW researchers. The two groups were pooled since response differences were <3% to most questions. Access to healthcare (36%), social determinants (33%) and health service infrastructure (21%) were the most common healthcare needs identified. Lack of research skills (design, analytic methods, training programs), capacity (coaching, mentoring), and funding were identified, as common research capacity needs. Both groups recommended research funding be directed to community (33%), health system (25%) or epidemiologic research. In phase 2, a focus group of 10 multidisciplinary participants (five female, five male) was formed. There were 16 valid comments with 21 points raised for saturation to be reached. Thematic content analysis and summaries were made at stages of the analysis. The four main thematic areas identified as barriers to health research, listed in order of frequency, were; Knowledge, Finance, Mentorship/Coaching, and Ethical Regulation/clearance, Incentives. Healthcare needs and barriers to research for CFIR in EA were identified in surveys of senior and junior East African HCW. Strategies, like MR, are addressing these capacity building issues.
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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 | 0.049 | 0.036 |
| 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 | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".