HOW ARE CHILDREN AND YOUNG PEOPLE ENGAGED IN RESEARCH ON PAEDIATRIC OBESITY AND WHICH ISSUES DO THEY REPORT? A SCOPING REVIEW
Bibliographic record
Abstract
The importance of engaging children and adolescents in research is increasingly acknowledged. The aim of this scoping review is to explore how children and young people have been engaged in research on paediatric obesity and which issues they have reported, in order to highlight areas that require further inquiry or action by researchers and health care professionals. There were 13 papers eligible for this review. Methods used included in-depth interviews, structured or semi-structured interviews, and focus groups, as well as more creative qualitative research methods. Half of the studies included young people with their parents; parents were always present when the interviewees were young children. Personal and sensitive themes, such as bullying, a desire to “fit in”, strong negative emotions about oneself (e.g., low self-esteem, low self-efficacy), and not feeling supported by family, peers, and professionals, were more often shared if parents were not present. An additional issue, wanting to be independent versus being under parental control was found in studies with adolescents. Engaging children and adolescents in multiple phases of research on paediatric obesity is beneficial in allowing better insight into their perspectives and providing recommendations that are more in line with their personal needs and life circumstances; such studies are still scarce in this field, however.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".