Predictors of Teacher Efficacy for Inclusive Practice in Pre-service Teachers
Bibliographic record
Abstract
Teachers who show high teacher efficacy affect student achievement positively. Teaching is sometimes seen as an overwhelming profession because of classroom diversity and expectations placed on teachers. It is important to bring beginning teachers to the point at which they feel they are capable and will be more emotionally equipped to take on the stressors of the classroom. The current study focused on predicting pre-service teachers’ efficacy for inclusive practice from variables found to be important in the literature: gender, inclusion-related beliefs, and experiences with individuals with disabilities. Participants consisted of 1,026 students completing the in-faculty component of their pre-service program in 9 faculties of education across Canada. They completed the Teacher Efficacy for Inclusive Practice survey and the Beliefs about Learning and Teaching Questionnaire. All teacher candidates appeared to benefit from experience with people with disabilities. General findings indicated more positive inclusive beliefs for women than men and for pre-service teachers in elementary than in secondary programs. Important differences emerged, however, concerning which beliefs contributed to each area of teacher efficacy for secondary as compared to elementary programs. Results are discussed in terms of issues to consider in initial teacher education programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".