Assessing Inclusion Quality: The SpeciaLink Early Childhood Inclusion Quality Scale
Bibliographic record
Abstract
Recent advances in policy development and professional practice in the field of early learning and child care have led to the expectation that it is appropriate and advantageous to include children with disabilities and extra support needs in early child care and learning programs. Yet, to date, evidence-based research on the effects of experiences in inclusive programs has been hampered by the lack of appropriate measures to assess inclusion quality that are reliable, valid, and relatively easy to administer. The purpose of the current study was to examine a newer measure, the SpeciaLink Early Childhood Inclusion Quality Scale (SECIQS), using data from 588 classrooms in child care centres and preschool programs across Canada. Through examination of inter-item consistency and reliability, along with exploratory and confirmatory factor analyses, evidence is provided for the utility and reliability of the measure. In addition, the validity of using both subscales is supported. Implications for policy and practice include recommending the use of all items in the SECIQS and scoring for all three factors in research studies. Further, separate \nsubscale scores for the Inclusion Principles and Inclusion Practices subscales are recommended as useful for centre assessments, quality improvement initiatives, and for educating the field about the contributors to inclusion effectiveness.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".