An Intervention to Co‐package Zinc and Oral Rehydration Salts (ORS) Improves Health Provider Prescription and Maternal Adherence to WHO‐recommended Diarrhea Treatment in Western Guatemala
Bibliographic record
Abstract
Background Diarrhea remains the second greatest cause of child morbidity and mortality in Guatemala, yet adherence to the WHO recommended treatment of Zinc (Zn) & ORS is low. Objective We evaluated the effectiveness of health‐facility‐level co‐packaging of Zn and ORS to improve health provider prescription practices and caregivers' adherence to the diarrhea treatment (2 days ORS; 10 days Zn) for children 2‐59 months of age in rural Guatemala. Methods Zn & ORS co‐packaging development was guided by social marketing and then evaluated in a community‐randomized intervention trial. The intervention group (IG) received Zn & ORS in a graphic co‐pack with instructions and provider messages for counseling in diarrhea treatment. The control group (CG) received normative care of Zn and ORS without co‐packaging or messages. Home‐monitoring of adherence was conducting at 5 and 10 days post‐prescription in 20 health posts in San Marcos, Province. Results Health providers in the IG were more likely to dispense both medications than those of the CG (aOR: 2.3; 95%CI: 1.0, 5.4). IG mothers (n=123) were more likely to give the full 10 days of zinc (aOR:1.7; 95%CI; 1.0, 2.8) than CG mothers (n=138), and IG mothers provided 1 more day of Zn (p<0.01). Conclusions The co‐packaging intervention improved both the prescription practices and adherence to zinc. This innovation could improve diarrhea treatment in Guatemala and holds potential utility in other situations of combined therapies. Research Support Grand Challenges Canada & Micronutrient Initiative
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".