Reliability of Anthropometric Measurement of Young Children with Parent Involvement
Bibliographic record
Abstract
Background: The purpose of this study was to determine the reliability of anthropometric measurements between two trained anthropometrists working in a team and one trained anthropometrist working with a child’s parent/caregiver in a primary health care setting. Study Design: An observational study to determine measurement reliability was conducted in a primary care child research network in Canada. In total, 120 children 0–5 years old had their anthropometric measurement taken twice by two trained anthropometrists working in a team and twice by one trained anthropometrist working with a child’s parent/caregiver. Inter- and intra-observer reliability was calculated using the technical error of measurement (TEM), relative TEM (%TEM), and the coefficient of reliability (R). Results: The %TEM values for length/height and weight were <2%, and the R coefficient values were >0.99, indicating a high degree of inter- and intra-observer reliability. The TEM values demonstrated a high degree of reliability for inter- and intra-observer measurement of length/height in comparison with other anthropometric measurement parameters. However, there was greater variation seen in the length measurement for children 0 to <2 years of age and in arm circumference measurement across both age-groups. Conclusion(s): This study suggests that anthropometric measurement taken by one trained anthropometrist with the assistance of a parent/caregiver is reliable. These findings provide evidence to support inclusion of a child’s parent/caregiver with anthropometric measurement collection in clinical setting(s) to enhance feasibility and efficiency and reduce the research costs of including a second trained anthropometrist.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".