Experiences of community members and researchers on community engagement in an Ecohealth project in South Africa and Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Community engagement (CE) models have provided much needed guidance for researchers to conceptualise and design engagement strategies for research projects. Most of the published strategies, however, still show very limited contribution of the community to the engagement process. One way of achieving this is to document experiences of community members in the CE processes during project implementation. The aim of our study was to explore the experiences of two research naïve communities, regarding a CE strategy collaboratively developed by researchers and study communities in a multicountry study. METHODS: The study was carried out in two research naïve communities; Gwanda, Zimbabwe and uMkhanyakude, South Africa. The multicentre study was a community based participatory ecohealth multicentre study. A qualitative case study approach was used to explore the CE strategy. Data was collected through Focus Group Discussions, Key Informant Interviews and Direct Observations. Data presented in this paper was collected at three stages of the community engagement process; soon after community entry, soon after sensitisation and during study implementation. Data was analysed through thematic analysis. RESULTS: The communities generally had positive experiences of the CE process. They felt that the continuous solicitation of their advice and preferences enabled them to significantly contribute to shaping the engagement process. Communities also perceived the CE process as having been flexible, and that the researchers had presented an open forum for sharing responsibilities in all decision making processes of the engagement process. CONCLUSIONS: This study has demonstrated that research naïve communities can significantly contribute to research processes if they are adequately engaged. The study also showed that if researchers put in maximum effort to demystify the research process, communities become empowered and participate as partners in research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".