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Record W4304606401 · doi:10.36615/jcsa.v37i2.1554

review of methodologies for research uptake in eco-health projects conducted in rural communities in Sub-Saharan Africa

2022· article· en· W4304606401 on OpenAlexfundno aff
Tafadzwa Mindu, Moses John Chimbari, Resign Gunda

Bibliographic record

VenueCommunicare Journal for Communication Studies in Africa · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-NataliNational Institute for Health and Care ResearchInternational Development Research CentreUNICEF
KeywordsContext (archaeology)IndigenousInterpersonal communicationPublic relationsMedical educationCommunity healthRural healthRural areaPsychologyMedicinePolitical scienceSociologyNursingGeographySocial sciencePublic health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.082
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.275
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0820.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0040.003
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.873
GPT teacher head0.676
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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