Teaching in an inclusive classroom with exceptional students: the influence on rural Manitoba teachers’ stress and self-efficacy
Bibliographic record
Abstract
The profession of teaching is said to be a stressful career. Nearly half (47%) of Canadian teachers leave their roles as educators before expected retirement due to high-stress levels and lack of support (Katz, 2015). Moreover, there have been evolutionary changes brought forward by the inclusive education model since the 1950s (Trudel, 2017). In understanding these changes and the inadvertent levels of burnout and attrition rates teachers are reportedly subject to, this study explored how (1) pre-service preparedness, (2) ongoing-professional development, (3) school-based support, and (4) resources affect teacher participants' stress and self-efficacy while working in an inclusive classroom with students who have exceptionalities. Exceptionalities in this study refer to students who qualify for either EBD2 or EBD3 funding within the province of Manitoba. Seven classroom teachers who worked in Manitoba rural schools were interviewed. Each teacher participant described their experiences working in an inclusive classroom with exceptional students and how the four critical areas explored had influenced their stress and self-efficacy in preparing for and working within these settings. This study used a thematic analytical approach to identify common themes among teacher participants’ descriptions about what factors have positively or negatively influenced their stress and self-efficacy working in these conditions and what changes they would like to see to enhance these areas positively. Key findings in this study have shown that lack of government funding has substantially impacted three out of four key areas and that there were equal levels of positive and negative identifiers altogether. Teacher participants have also identified coping strategies to manage their stress and self-efficacy to remain within their professions despite the daily challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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".