Effects of an Inclusive Childcare Capacity Building Program on In-service Teachers’ Teaching Efficacy and Attitudes towards Inclusion
Bibliographic record
Abstract
An inclusive childcare capacity building program was designed to enhance teacher efficacy and attitudes toward the inclusion of childcare teachers. A total of 36 teachers working in childcare facilities in Gyeonggi-do participated in this study. Nineteen teachers were assigned to the treatment condition and 17 teachers were assigned to the control condition. The program explicitly addressed 15 topics, including screening and diagnosis, early development, characteristics of disability, family support and related services, IEP, and online site observation. The program was primarily delivered by field professionals using adult learning techniques such as small group activities, reflective journal writings, and online consultation. Teachers in the treatment group outperformed teachers in the control group on three measures of teacher efficacy, teacher play efficacy, and attitudes toward inclusion. The results were statistically significant. The increase in teacher efficacy, play efficacy, and attitudes toward inclusion of teachers was attributed to the well-designed program, encompassing early development, disability characteristics, small group discussions, and online support. The results highlight the importance of providing and disseminating a capacity building program for in-service teachers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".