Accuracy of Child Growth-Monitoring Weights Obtained by Commune Volunteers in Phu Tho Province, Vietnam
Bibliographic record
Abstract
BACKGROUND: Weight-for-age is a commonly used indicator of the health of children and communities. We determined the accuracy of health volunteers' weight measurements in a nutrition project in Vietnam. OBJECTIVE: To report the accuracy of the volunteers' weight measurements and to assess the likely effect of any inaccuracies. METHODS: Save the Children /USA trained health volunteers to weigh children (6-36 months old) every other month from December 1999 to August 2000. Trained researchers randomly rechecked 257 weights (range, 24-114 per session). We computed nondirectional and directional differences between the weights measured by volunteers and those measured by researchers. RESULTS: The weights recorded by volunteers were lower than those recorded by researchers by an average of 30 g (p < .05). Almost all of the error occurred during the first weighing session, at which the average weight recorded by volunteers was 280 g below that recorded by researchers (p = .01). The error at subsequent weighings was minimal (< 20 g below reference at each session). CONCLUSIONS: One-time directional error suggests bias. Perhaps some communities (or families) influenced the volunteers to report weights lower than those actually observed to justify the programmatic food supplements or to give the impression at subsequent weighings that the level of malnutrition had been successfully reduced from that at the first session. Careful supervision of measurements of weight at baseline is essential.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".