How Do Attitudes and Self-Efficacy Predict Teachers’ Intentions to Use Inclusive Practices? A Cross-National Comparison Between Canada, Germany, Greece, Italy, and Switzerland
Bibliographic record
Abstract
Inclusive education is a key goal of modern educational reforms, yet its implementation is complex. This study examines the roles of teacher attitudes and self-efficacy in predicting their intentions to use inclusive practices across five western countries: Canada, Germany, Greece, Italy, and Switzerland. The study identified both significant differences and commonalities in prediction patterns across these countries. For instance, beliefs about inclusion varied in their significance, being the most influential predictor among Italian teachers, while managing challenging behaviour was a key predictor for Swiss teachers only. For the other predictors, no significant differences were found, and self-efficacy in collaboration was the strongest predictor nominally. The study suggests that, while aspects such as collaboration seem generally important across countries, effective strategies for promoting inclusive education may also need to be tailored to each country’s unique context, considering aspects of historical background of inclusive education, teacher training, and support. It also emphasizes the need to consider domain-specific aspects of teacher self-efficacy, as different facets differently affect teachers’ intentions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".