High school teachers' attitudes towards inclusion: a Canadian perspective
Bibliographic record
Abstract
This study investigated Canadian high school teachers’ attitudes towards inclusion of students with special education needs into the general classroom environment. Teachers’ cognitive, affective and behavioral attitudes towards inclusion were examined. Data was collected using a 7-point Likert scale survey called the Inclusion Scale for High School Teachers created by Dr. Catherine Ernst (2006). The survey, which included a demographic questionnaire as well as cognitive, affective and behavioral attitude statements regarding inclusive practices, was conducted with a population of 150 high school teachers from a single urban school division in a large city in central Canada. Participants’ demographic information was analyzed using descriptive statistics. The Somers' Delta (Somers’ D) statistic was used to determine the strength and relatedness of independent variables of teacher demographics and school environmental variables with the dependent variable of teacher attitude. Findings showed that high school teachers’ attitudes towards inclusion were more positive than negative. It was found that teachers’ behavioral attitudes towards inclusion were most positive while their affective attitudes towards inclusion were least positive. The demographic variables with the greatest influence on teacher attitudes towards inclusion were: (a) experience as lead teacher in an inclusive setting, (b) access to human resources and supports, and (c) professional development and training related to inclusion. This study is of particular importance as it is the first study to focus specifically on Canadian high school teachers’ attitudes towards inclusion.\n\n\t\tKeywords: high school, teachers, inclusion, attitude
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".