Informed Consent through Community Engagement in Collaborative Research in Developing Countries
Bibliographic record
Abstract
Multinational nature of research activities has been growing increasingly through collaboration that involves a developing country and a developed country. However, several scandals have been reported to date in such research done by the western authorities in the name of collaboration, development, or health improvement in different developing countries especially revolving informed consent and protection of the participants. Those incidences tend to create distrust and may result in non-cooperative attitude among developing countries in further collaboration. This paper aims to discuss how much an informed consent is really informed and how community engagement can make it more meaningful and ethical by respecting the values of any society (i.e., participating developing country). Evidence suggests that there are essential interdependence and overlapping between consenting process and community engagement in that collaborative research. Community engagement is able to provide a meaningful insight that helps in formulation of context-specific consent process. It also helps to regulate and monitor consenting procedure, withdrawal from participation, and any relevant changes while research is ongoing. Moreover, as a sign of showing respect to the participating group in research, community engagement has been found instrumental in making research more acceptable and mutually beneficial. CBMJ 2023 July: Vol. 12 No. 02 P: 192-200
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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 | 0.416 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".