The Use of Implementation Science Tools to Design, Implement, and Monitor a Community-Based mHealth Intervention for Child Health in the Amazon
Bibliographic record
Abstract
It is essential to analyze the local context and implementation components to effectively deliver evidence-based solutions to public health problems. Tools provided by the field of implementation science can guide researchers through a comprehensive implementation process, making innovations more adaptable, replicable, and sustainable. It is equally important to report on the design and implementation process so others can analyze, replicate, and improve on the progress made from an intervention. Reporting on the implementation process has been especially limited in global health settings, where it is most needed due to the high burden of illness and the complexity of their settings. The current study applies tools of implementation science to improve the implementation and reporting of an integrated intervention for child health and development in a global health setting. METHODS The study reports on the implementation of the Child Health Education and Surveillance Tool App (CHEST App), in the Amazon of Peru. The study uses the Active Implementation Frameworks (AIF) to design, implement, adapt, and monitor the CHEST App intervention. RESULTS During the Exploration Stage, the research team gathered evidence to identify the primary drivers of poor nutrition and child development in the communities, and analyzed potential evidence-based solutions to address the problem. During the Installation Stage, the intervention and the implementation protocol were co-created with participants in the field. Also, the capacity to implement the intervention by the providers in the communities was assessed and supported. During the Initial Implementation Stage, the research team deployed the intervention and conducted improvement cycles through information gathering and analysis. Multiple design iterations and rapid-cycle problem solving were used to improve the functionality and acceptability of the intervention and implementation strategy. CONCLUSION Design, adaptation, and implementation of the intervention was guided by the AIF. The stages provided guidance to ensure the local setting was taken into full consideration and the intervention was co-created by the recipients. By reporting on the implementation process, the study contributes to the knowledge base needed to improve the impact and scalability of child health programs in global health settings.
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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 | 0.285 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".