Reaching the Hard to Reach: Mobile Development Screening Van to the Rescue
Bibliographic record
Abstract
Currently more than 15% of children have an emotional, behavioral, or developmental concern. In spite of recommendations by the American Academy of Pediatrics and the Centers for Disease Control and Prevention to conduct universal developmental surveillance and screening with children at 9, 18, and 24 months of age, fewer than 30% of children under 6 years of age ever receive a developmental screening. Children in low-income and diverse communities are even less likely to be screened by a medical provider and/or referred for further diagnostic evaluation when predictive concerns are identified. As part of a cross-agency collaborative effort involving a family resource center, a child care resource and referral agency, a regional center for developmental disabilities, and a master's degree program in early childhood education, the Mobile Developmental Screening Van Project conducted outreach to provide free developmental screening with families of children 0-8 years of age in diverse and low-income communities within the greater Los Angeles County. Using the Parent Evaluation of Developmental Status (PEDS) screening tool, 94 children were screened over the course of 6 months, with 33% showing 2 or more predictive concerns that resulted in referrals for further diagnostic evaluation. The feasibility of reaching families in hard to reach communities using a mobile screening van, as well as study limitations and recommendations for next steps, are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".