Professionals' Judgments of Peer Interaction Interventions: A Survey of Members of the Division for Early Childhood
Bibliographic record
Abstract
We surveyed a sample of the membership of the Division for Early Childhood (DEC) of the Council for Exceptional Children (CEC) with the Social Interaction Program Features Questionnaire-Revised (SIPFQ-R) to determine their judgments of the acceptability, feasibility, and current use of contemporary peer interaction intervention tactics and strategies. We analyzed resultant information descriptively and with MANOVA procedures. In addition, we collected respondents' perspectives about barriers to the implementation of social interaction interventions and the proportion of preschool children they believed could benefit from peer interaction interventions. Results indicated that DEC members judged a majority of social interaction intervention tactics and strategies as acceptable but members rated many of those interventions as less feasible and reported them to have relatively lower rates of use. We believe the findings indicate that a continuing research to practice gap exists for peer interaction interventions in early childhood special education (cf. Brown & Conroy, 2001). Given that gap, we call for targeted professional development activities focused on a range of social interaction intervention tactics and strategies and renewed research efforts to refine and develop teacher friendly intervention strategies for practitioners who work in community-based preschools.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".