The predictive utility of a brief kindergarten screening measure of child behavior problems.
Bibliographic record
Abstract
OBJECTIVE: Kindergarten teacher ratings, such as those from the Teacher Observation of Classroom Adaptation-Revised (TOCA-R), are a promising cost- and time-effective screening method to identify children at risk for later problems. Previous research with the TOCA-R has been mainly limited to outcomes in a single domain measured during elementary school. The goal of the current study was to examine the ability of TOCA-R sum scores to predict outcomes in multiple domains across distinct developmental periods (i.e., late childhood, middle adolescence, late adolescence). METHOD: We used data from the Fast Track Project, a large multisite study with children at risk for conduct problems (n = 752; M age at start of study = 6.55 years; 57.7% male; 49.9% Caucasian, 46.3% African American). Kindergarten TOCA-R sum scores were used as the predictor in regression analyses; outcomes included school difficulties, externalizing diagnoses and symptom counts, and substance use. RESULTS: TOCA-R sum scores predicted school outcomes at all time points, diagnosis of ADHD in 9th grade, several externalizing disorder symptom counts, and cigarette use in 12th grade. CONCLUSIONS: The findings demonstrate the predictive utility of the TOCA-R when examining outcomes within the school setting. Therefore, these results suggest the 10-item TOCA-R may provide a quick and accurate screening of children at risk for later problems. Implications for prevention and intervention programs are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".