Engaging Primary Health Care Providers in the Promotion of Health Weight among Young Children: Barriers and Enablers for Policy and Management
Bibliographic record
Abstract
Recent national efforts to stem the alarming growth of childhood overweight and obesity in Australia have highlighted the importance of preventative strategies that focus beyond the child-on parents, families, primary health care providers (PHCPs) and child care services. But, while such efforts have to date focused on school-based interventions, once poor eating habits and sedentary lifestyles have set in, a growing body of research is indicating that PHCPs can play a more influential role in monitoring and modifying factors affecting prevention and early intervention in preschool children. This paper presents the findings of a systematic review to: (a) identify key barriers to the effective engagement of PHCPs with parents and child care staff in the promotion of healthy weight among children aged 2-6 years, (b) appraise "promising" interventions for strengthening the capacity of PHCPs to effectively deal with these barriers, and (c) synthesise policy options to encourage and engage PHCPs. The study draws on the lessons of promising interventions to highlight the urgent need to address organisational, attitudinal, knowledge, skills and training barriers, to facilitate the engagement of PHCPs in different settings-based environments (clinical, child care, home and community).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".