An mHealth tool for community health workers to improve caregiver knowledge of child health in the Amazon: An effectiveness-implementation hybrid evaluation
Bibliographic record
Abstract
When community health workers (CHWs) are effective, they can teach healthy child rearing practices in their communities and improve child health and development outcomes. An effective mHealth tool can improve the capacity of CHWs to transmit knowledge to caregivers. This article evaluates the implementation of an mHealth tool in a CHW program in the Amazon of Peru. The intervention was designed, implemented, and evaluated with the guidance of multiple implementation science tools. A Hybrid Type 3 evaluation design was used to test the effectiveness of the implementation strategies and appropriateness of the intervention. The implementation outcomes: acceptability, adoption, dosage, and fidelity were analyzed with mixed methods approach to determine if the intervention was successfully installed in the CHW program. The service outcome, knowledge scores, was analyzed with an independent samples t-test and one way ANOVA to determine the effect of the program. The implementation strategies resulted in high degrees of acceptability, adoption, and fidelity of the mHealth tool. The surveillance component of the mHealth tools was not adequately adopted. The group of caregivers that received home visits with the mHealth tool (N = 48) had significantly higher knowledge scores (+1.26 standard deviations) than those in the control group (N = 138) (t(184) = -4.39, p<0.001). The COVID-19 pandemic significantly decreased the dosage of the intervention received by the participants. The CHEST App intervention is a promising tool to improve the capacity of CHWs during their home visits. Trial registered with ISRCTN on 11/29/2018 at https://doi.org/10.1186/ISRCTN43591826.
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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 | 0.056 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".