Mentorship of Special Education Teachers of Students who have Treatment and Rehabilitation Needs
Bibliographic record
Abstract
This study examines the role of mentorship in enhancing the self-efficacy of special education teachers working with students requiring treatment and rehabilitation. It also explores how mentorship supports the implementation of the new Ontario Language (OME, 2023) curriculum within Education and Community Partnership Programs (ECPP). A qualitative case study approach was used. Semi-structured interviews were conducted with three experienced mentors and one mentee from a publicly funded Ontario school board. Data were analyzed using thematic analysis to identify key themes related to mentorship and teacher self-efficacy. The study identified four major themes: teacher isolation, challenges of language and literacy instruction and the new Ontario Language curriculum, congruous mentorship support in ECPP settings, and informal communications and responsive mentorship. Findings highlight the importance of trust-building and technology in mentorship. This study is relevant to educators, policymakers, and administrators in special education, mentorship programs, and alternative education settings. It provides insights for improving teacher support systems within ECPP and similar learning environments. This study contributes to the limited research on mentorship for special education teachers in ECPP. It highlights the role of mentorship in overcoming professional isolation, improving instructional practices, and integrating technology to enhance teacher development.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".