A scoping review of school‐based anthropometric measurement
Bibliographic record
Abstract
Though anthropometric measurement (AM) frequently occurs in school settings, it is not without risks to child wellbeing. The aim of this scoping review was to examine how AM in school settings takes place and is reported on to make recommendations on best practices. We identified and extracted data from 440 studies published since 2005 that conducted AM in school (pre-school through secondary/high school) settings. Privacy and sensitivity of AM were unclear in over 90% of studies. Thirty-one studies (7.0%) reported protecting student privacy, while nine (2.0%) reported public measurement. Only five studies reported sensitivity regarding AM (1.1%). Exactly who conducted AM was not specified in 201 studies (45.7%). Sixty-nine studies did not provide a weight status criteria citation (19.2%), and 10 used an incorrect citation (2.7%). In summary, serious shortcomings in the reporting of how AM is conducted and by whom, along with details concerning weight status classification, are evident. There is considerable room for improvement regarding the reporting of key methodological details. We propose best practices for AM in school settings, which also double as conditions that should be met before AM takes place in school settings.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".