The Learn at Play Program (LAPP): Merging Family, Developmental Research, Early Intervention, and Policy Goals for Children with Down Syndrome
Bibliographic record
Abstract
Abstract This article addresses the dynamics of parent–child interactions and their potential influence on the development of social competence among children with Down syndrome (DS). The authors argue that a strong parent–child relationship is fundamental for building the social competence of children with DS and the integration and inclusion of these children into their communities. The Learn at Play Program (LAPP), a model of early intervention that prioritizes the goals of nurturing and shaping the development of interpersonal skills and social competence among children with DS, is proffered. Discussed first is a brief overview of early intervention and the rationale for the need to focus on parent–child interactions and social competence when DS is present. The LAPP early intervention model for children with DS and their families is presented along with data from seven mother–child dyads assessed with the LAPP longitudinal study of parent–child interactions in DS to demonstrate the use of the model. The author's experiences with the LAPP program are also used to illustrate the utility of linking supportive networks (such as nonprofit organizations), academic and provincial government funding partnerships and public policy forums, and publicly funded organizations providing services to children with developmental delays from birth to 3 years old.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".