Multicountry review: developmental surveillance, assessment and care by outpatient paediatricians
Bibliographic record
Abstract
BACKGROUND: Care of young children with neurodevelopmental disorders (NDD) is a major component of paediatric outpatient practice. However, cross-country practice reviews to date have been limited, and available data demonstrate missed opportunities for early identification, particularly in vulnerable population subgroups. METHODS: Multicountry review of national paediatric body guidance related to developmental surveillance, early identification and early childhood intervention together with review of outpatient paediatrician practices for developmental assessment of children aged 0-5 years with/at risk of NDDs. Review included five countries with comparable nationalised universal child healthcare systems (ie, Australia, Canada, New Zealand, Sweden and the UK). Data were collected using a combination of published and grey literature review, supplemented by additional local sources with descriptive review of relevant data points. RESULTS: Countries had broadly similar systems for early identification of young children with NDDs alongside universal child health surveillance. However, variation existed in national paediatric guidance, paediatric developmental training and practice, including variable roles of paediatricians in developmental surveillance at primary care level. Data on coverage of developmental surveillance, content and quality of paediatric development assessment practices were notably lacking. CONCLUSION: Paediatricians play an important role in ensuring equitable access to early identification and intervention for young children with/at risk of NDDs. However, strengthening paediatric outpatient care of children with NDD requires clearer guidance across contexts; training that is responsive to shifting roles within interdisciplinary models of developmental assessment and improved data to enhance equity and quality of developmental assessment for children with/at risk of NDDs.
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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 | 0.024 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".