The Paediatrician’s role in understanding and supporting parenting practices through a health behaviour lens
Bibliographic record
Abstract
> What’s the right bedtime for my child? > > How do we cut down on junk food at home? > > How do I decrease my child’s “screen time”? > > When my child has an “episode”, should I give a “time out”? Today, parents can access a myriad of online resources for advice on strategies to support their child’s health and development. A large proportion of families present annually to their doctor with concerns that require counselling around parenting behaviours, including management of obesity, sleep hygiene, screen time practices, reading and literacy at home, and behaviour management. Indeed, for many of these behaviours, there exist evidence-based recommendations or consensus guidelines that paediatricians can use to advise their patients’ parents. However, it is important to consider how comfortable paediatricians are in providing this advice, and furthermore, how effective they are in helping families to change their parenting behaviours. Modification of these behaviours is known to impact long-term health outcomes. Households with excessive screen time exposure are associated with increased risk of speech delay, obesity and sleep problems.1–3 Likewise, families who incorporate structured bedtime reading routines are associated with improved social behaviours, emotional and behavioural regulation, sleep, cognitive development and school readiness.4 We understand that caregiving practices are deeply ingrained, being impacted by a multitude of factors such as parents’ own families of origin, culture, education background, parental mental health and financial resources. There is well-established literature and numerous evidence-based parenting programmes that delineate key features of how parents can encourage positive behaviours …
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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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".