review of methodologies for research uptake in eco-health projects conducted in rural communities in Sub-Saharan Africa
Bibliographic record
Abstract
This review analyses research uptake methods which have been used by researchers in sub-Saharan Africa to determine which methods are effective for communities. The key area of thestudy is research uptake methods applicable at rural community level. The study analyses howeffective these methods are in getting research findings adopted by the community, stirringbehaviour change and raising awareness about a problem. The review makes recommendationsfor research projects that seek to conduct research uptake in the rural areas of sub-Saharan Africa.A systematic search for articles was done using Medline, PubMed and Google Scholar. Articleson the uptake of eco-health research findings at a community level were screened and analysedusing narrative synthesis. Results showed that strategies involving media, educational materialsand interpersonal communication with the communities worked most effectively. Some examplesof these were use of radio programmes, film productions, community theatre, field workers,community meetings, educational programmes, peer education and point of care displays. Thestudy concluded that to enhance research uptake in communities, innovative methods whichcapture the context of the communities involved need to be used. Selected strategies should usethe eco-health approach, engage the community and incorporate indigenous knowledge systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".