Perceptions and experiences of community-networks that facilitate engagement in health research: Ifakara Health Institute-Bagamoyo case-study
Bibliographic record
Abstract
Involvement of communities in the field of health research collectively known as community engagement is considered as ethical conduct of research. At the Ifakara Health Research Institute (IHI) in Bagamoyo, Tanzania, nothing has been documented on how the engagement is being done and what community structures/networks are involved in the facilitation of engagement activities, and what are the systematic functioning of these structures since the formulation of community advisory board (CAB) in 2007. In this study six focus group discussions (FGDs) and 19 in-depth interviews (IDIs) among respondents participated in IHI research for the past five years were performed. Furthermore, focus groups and in-depths interview were audiotaped, transcribed, and analyzed using framework analysis techniques. This study found that; engagement was more likely being influenced by the type of research project and kind of participants needed, different community networks such as village executive officers, community health workers, hamlet leaders, and community advisory boards were the key stakeholders and; community-level public meetings, household visitation and informationgiving sessions at the health facilities were the main approaches used during engagement processes. However, it was found that they did not reach most of the target people due to limited levels of interaction with potential participants, there are no central coordination of the engagement activities at the Institute, different research projects at the same Institute have been approaching these structures separately, little engagement, misunderstanding of the research objectives have been reported in contributing to the participants dropout. This study recommends that there is a need of developing a community engagement unit that would work across projects to support engagement with the community.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".