Initiating community engagement in an ecohealth research project in Southern Africa
Bibliographic record
Abstract
BACKGROUND: Community Engagement (CE) in health research ensures that research is consistent with the socio-cultural, political and economic contexts where the research is conducted. The greatest challenges for researchers are the practical aspects of CE in multicentre health research. This study describes the CE in an ecohealth community-based research project focusing on two vulnerable and research naive rural communities. METHODS: A qualitative, longitudinal multiple case study approach was used. Data was collected through Participatory Rural Appraisals, Focus Group Discussions, In-depth Interviews, and observations. RESULTS: The two sites had different cultural values, research literacy levels, and political and administrative structures. The engagement process included 1) introductions to the administrative and political leaders of the area; 2) establishing a community advisory mechanism; 3) community empowerment and 4) initiating sustainable post-study activities. In both sites the study employed community liaison officers to facilitate the community entry and obtaining letters of permission. Both sites opted to form Community Advisory Boards as their main advisory mechanism together with direct advice from community leaders. Empowerment was achieved through the education of ordinary community members at biannual meetings, employment of community research assistants and utilising citizen science. Through the research assistants and the citizen science group, the study has managed to initiate activities that the community will continue to utilise after the study ends. General strategies developed are similar in principle, but implementation and emphasis of various aspects differed in the two communities. CONCLUSIONS: We conclude that it is critical that community engagement be consistent with community values and attitudes, and considers community resources and capacity. A CE strategy fully involving the community is constrained by community research literacy levels, time and resources, but creates a conducive research environment.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".