Three Complementary Community-Based Approaches to the Early Identification of Young Children at Risk for Developmental Delays/Disorders
Bibliographic record
Abstract
This article discusses 3 complementary approaches to the identification of young children at risk for developmental delays. The first is a longitudinal follow-up program that targets and tracks the development of infants admitted to neonatal intensive care units. The second approach is designed to identify children with neuromotor delays from birth to 36 months by testing the validity of a new screening measure and comparing traditional and online instructional techniques to teach professionals how to use the instrument. The third approach is a community-based, universal, developmental screening project that also examines the impact of this project on the community's capacity for early identification and intervention with young children. The article reports on the goals, objectives, research questions, methodology, and early results of these 3 approaches. These approaches are part of a larger collaborative interdisciplinary, ecological, community/university research initiative studying early child development in British Columbia, Canada. Drawing on a wide range of university-based health, medical, and social science researchers working in close collaboration with community-based early intervention programs, the article discusses the 3 approaches as points along a continuum of longitudinal follow-up, targeted, and universal screening early identification programs and also examines the “value added” of conducting these studies under the umbrella of one overall program of research. On the basis of the findings of the 3 studies, we propose an integrated framework for the surveillance, screening, and early identification of young children.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".