Analysis of the implementation of a community-based intervention to control dengue fever in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: A community-based dengue fever intervention was implemented in Burkina Faso in 2017. The results achieved vary from one area to another. The objective of this article is to analyze the implementation of this intervention, to better understand the process, and to explain the contextual elements of performance variations in implementation. METHODOLOGY: The research was conducted in the former sector 22 of the city of Ouagadougou. We adapted the Consolidated Framework for Implementation Research (CFIR) to take into account the realities of the context and the intervention. The data collected from the participants directly involved in the implementation using three techniques: document consultation, individual interview, and focus group. RESULTS: Two dimensions of CFIR emerge from the results as having had a positive influence on the implementation: (i) the characteristics of the intervention and (ii) the processes of the intervention implementation. The majority of the CFIR constructions were considered to have had a positive effect on implementation. The quality and strength of the evidence received the highest score. The dimension of the external context had a negative influence on the implementation of the intervention. CONCLUSION: The objective of the study was to analyze the influence of contextual elements on the implementation process of a community-based dengue fever intervention. We used the CFIR framework already used by many studies for implementation analysis. Although it was not possible to test this framework in its entirety, it is useful for the analysis of the implementation. Its use is simple and does not require any special skills from users. Usability is indeed an essential criterion for the relevance of using an analytical framework in implementation science.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".