Enhancing Child Care for Children with Special Needs Through Technical Assistance
Bibliographic record
Abstract
Children with special needs often require additional supports in child care settings. The provision of technical assistance (TA) and consultation to child care teachers is an established method for addressing this need. This study expands on existing research by bringing the perspective of different adults (parents, technical assistance consultants, teachers, and child care center directors) together to better understand the experiences of all parties involved in TA cases for children between the ages of three and five. The concerns most frequently leading to the consultation were social-emotional-behavioral (50.5%), developmental (32.3%), medical (28.3%), and environmental risk (14%), and one quarter of parents reported that their child had more than one of these concerns. Parents’ evaluations of the outcomes of the consultation were predicted by the parent’s race, level of education, and whether they saw a behavioral concern as the initial reason for the consultation. Open-ended comments provided more insight into each group of adults’ experiences, some of which included frustration about feeling involved/included in the consultation (for parents) and parents’ not being involved in and/or engaged with the consultation (for other adults). The study’s findings emphasize the importance of all adults working as a team to ensure the best possible care for children with special needs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".