Parent perceptions of routine growth monitoring: A scoping review
Bibliographic record
Abstract
BACKGROUND: Despite being a well-accepted part of paediatric care, little is known about the benefits or potential harms of routine growth monitoring (RGM) from a parent's perspective. OBJECTIVE: To explore parental experiences with RGM. METHODS: Literature searches were performed on Google Scholar, psycINFO, CINAHL, and PubMed. Included studies were published after 2000 and described parental comprehension, preferences, attitudes toward, and/or behaviour change related to RGM. RESULTS: Of 24 reviewed studies, four themes were identified: reliance on growth monitoring, understanding, influence on feeding and behaviour, and response to obesity-related classification. RGM was familiar but not strongly preferred to identify a child's weight status. Parental understanding of RGM was poor, particularly among parents with low socioeconomic status. A common belief was that heavier babies were healthier, while smaller babies should prompt concern. Parents may be anxious and change behaviour in response to RGM, such as by halting breastfeeding, supplementing, or restricting their child's diet. Parents frequently discounted RGM information when their child was identified as overweight, and expressed concerns about self-esteem and eating disorders. CONCLUSION: This scoping review identifies that although RGM is familiar and sometimes reassuring to parents, increased consideration should be given to potential harms from parental perspectives when conducting growth monitoring.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".