Instrument Development and Validation of the Home Child Care Version of the Assessment for Quality Improvement
Bibliographic record
Abstract
Research Findings: Measures of home child care (HCC) quality are limited and tend to be labor intensive. This article presents the measure development process as well as psychometric, construct, convergent and discriminant validity analyses for the HCC version of the Assessment for Quality Improvement (HCC-AQI) measure. The HCC-AQI is part of a suite of observational measures developed by the City of Toronto for use in its early childhood education and care Quality Rating and Improvement System. It takes 60–90 minutes to administer, making it significantly more efficient than other measures. Instrument development involved expert panels and item response theory analyses. Exploratory factor analyses and internal consistency analyses indicate that the HCC-AQI measure can be categorized into two subscales: Physical Space and Experiences and Caregiver/Child Interactions. Moderately strong correlations between these subscales also support computing total HCC-AQI scores. Correlations between the Infant/Toddler and Early Childhood Home Observation for Measurement of the Environment and the Responsive Interactions for Learning scale were moderate, providing evidence for convergent validity. Practice or Policy: the HCC-AQI is a promising efficient measure of HCC that can be used for research as well as quality improvement and accountability purposes.
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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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".