When communities are really in control: ethical issues surrounding community mobilisation for dengue prevention in Mexico and Nicaragua
Bibliographic record
Abstract
We discuss two ethical issues raised by Camino Verde, a 2011-2012 cluster-randomised controlled trial in Mexico and Nicaragua, that reduced dengue risk though community mobilisation. The issues arise from the approach adopted by the intervention, one called Socialisation of Evidence for Participatory Action. Community volunteer teams informed householders of evidence about dengue, its costs and the life-cycle of Aedes aegypti mosquitoes, while showing them the mosquito larvae in their own water receptacles, without prescribing solutions. Each community responded in an informed manner but on its own terms. The approach involves partnerships with communities, presenting evidence in a way that brings conflicting views and interests to the surface and encourages communities themselves to deal with the resulting tensions.One such tension is that between individual and community rights. This tension can be resolved creatively in concrete day-to-day circumstances provided those seeking to persuade their neighbours to join in efforts to benefit community health do so in an atmosphere of dialogue and with respect for personal autonomy.A second tension arises between researchers' responsibilities for ethical conduct of research and community autonomy in the conduct of an intervention. An ethic of respect for individual and community autonomy must infuse community intervention research from its inception, because as researchers succeed in fostering community self-determination their direct influence in ethical matters diminishes. TRIAL REGISTRATION: ISRCTN 27581154.
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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 | 0.057 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".