Mobilizing Knowledge: A Comprehensive Toolkit for Quality Assured Nutrition Data and Piloting Gender Indicators
Bibliographic record
Abstract
Nutrition International (NI) sought to standardize and add novel indicators to the multiple coverage surveys conducted each year on maternal, newborn, infant and child nutrition programs to assure quality and timely, gender-related data that meets next-generation monitoring needs. In collaboration with Campbell Collaboration (CC) and University of Toronto (UT), NI developed a comprehensive step-wise survey toolkit with multiple intervention modules using a systematic process for selecting and contextualizing indicators (NI), validating data quality (NI-UT) and piloting gender indicators derived based on statistical modelling results using Demographic and Health Survey gender and nutrition related data (CC-NI). The resulting Nutrition Information Monitoring Systems (NIMS) toolkit now includes ODK formatted questionnaire templates, quality control and assurance checklists and ready-to-use SPSS syntax for data analysis and interpretation purposes. NIMS derives its results from household-level information through the following modules: zinc and Oral Rehydration Salts coverage for diarrhea treatment, maternal-newborn nutrition, weekly iron-folic acid supplementation among adolescent girls, and infant and young child nutrition – each with selected knowledge components and newly-devised gender-related questions to inform NI's nutrition-sensitive programs. Selected modules were implemented in NI's ten intervention countries 2019–20. Application of these procedures and quality metrics allowed NI program officers to: 1) systematically assess quality during data collection – identifying and correcting surveyor errors and potential sampling bias in a timely fashion, 2) validate and visually demonstrate data quality to relevant stakeholders, and 3) produce quality assured data within 1–3 weeks, compared to 1–3 months for previous surveys that did not use the NIMS procedures and tools. This systematic approach facilitated reporting timely, quality assured nutrition program data to inform how NI interventions and gender-related analyses will identify how NI's programming can be more gender-responsive. CanWaCH, Global Affairs Canada and Nutrition International.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".