Taking innovative vector control interventions in urban Latin America to scale: lessons learnt from multi-country implementation research
Bibliographic record
Abstract
Prior to the current public health emergency following the emergence of chikungunya and Zika Virus Disease in the Americas during 2014 and 2015, multi-country research investigated between 2011 and 2013 the efficacy of novel Aedes aegypti intervention packages through cluster randomised controlled trials in four Latin-American cities: Fortaleza (Brazil); Girardot (Colombia), Acapulco (Mexico) and Salto (Uruguay). Results from the trials led to a scaling up effort of the interventions at city levels. Scaling up refers to deliberate efforts to increase the impact of successfully tested health interventions to benefit more people and foster policy and program development in a sustainable way. The different scenarios represent examples for a 'vertical approach' and a 'horizontal approach'. This paper presents the analysis of a preliminary process evaluation of the scaling up efforts in the mentioned cites, with a focus on challenges and enabling factors encountered by the research teams, analysing the main social, political, administrative, financial and acceptance factors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".