A Collaborative Approach to Early Identification and Referral of Children Who are in Family Childcare Settings, Birth to Five, Born to Teenage Mothers
Bibliographic record
Abstract
Children of teenage mothers are at high risk for developmental delays, intellectual and learning disabilities, behavior disorders and school related problems [1]. Early identification and referral into prevention or early intervention programs may ameliorate that risk. Children of teen mothers who are in family childcare may not have access to routine developmental and behavioral screenings that would lead to early identification and referral. Members of an early childhood advisory board collaborated to conduct a screening event using the ASQ-3® and the ASQ:SE® at a local children’s museum for 26 children of teen mothers who had no previous access to developmental screenings. Parents completed the questionnaires while playing with their children at the museum. Nine of the children scored well within the range of typical development and 17 scored at or beyond the cut-off scores on the ASQ-3 and/or the ASQ:SE. Each child who scored at or beyond the cut-off received referrals for evaluation, parent and child programming and/or family support services. When there were concerns, families also received care coordination while all families received activities and a child’s book. The implications for this study suggest that collaborative efforts are effective in providing access to developmental screenings and referral into subsequent services for at-risk young children who are in family childcare settings.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".