Evaluation of a statewide initiative to reduce expulsion of young children
Bibliographic record
Abstract
This program evaluation study describes 3 years of implementation of Arkansas's BehaviorHelp (BH) system, a statewide expulsion prevention support system for early care and education (ECE). BH coordinates three tiers of supports to ECE professionals, including phone support, on-site technical assistance (TA), and infant and early childhood mental health consultation (IECMHC). We examine differences in characteristics of those served across BH service tiers, describe short-term case outcomes, and explore factors associated with expulsions. BH accepted referrals for 1,195 children in 488 ECE programs. The majority of referrals involved male children over the age of three, and most cases were assigned to the TA tier (68.5%). Cases assigned to receive IECMHC (28.4%) were more likely to involve children in foster care, receiving developmental therapies, and with higher rates of exposure to potentially traumatic events. The expulsion rate among referred children was 2.9%, and reported teacher engagement with the support process was high. Teachers receiving IECMHC services reported significant improvements in children's symptoms of emotional and behavioral problems. Exploratory analyses revealed that risk factors for expulsion included being a male, in foster care, in a lower quality ECE environment, and having a teacher with less training in social-emotional development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".