Special Education Teachers and Mental Health Professionals: Collaborating to Support Students with Mental Health Issues in the Clasroom
Bibliographic record
Abstract
In Ontario, teachers are increasingly expected to take on the role of front-line professionals who participate in early mental health interventions to support students in the classroom. Literature reveals a lack of research conducted on the frequency and effectiveness of the collaboration between teachers and mental health professionals. This qualitative research study is aimed to explore this topic further. Data was collected through semi-structured interviews with two special education teachers and a mental health professional. Findings suggest that weekly face-to-face meetings with a multi-disciplinary team and ongoing dialogue between special education teachers and mental health professionals are beneficial. This collaboration can result in numerous social benefits for students with mental health issues such as improved self-esteem, and academic benefits such as greater individual programming. Participants reported challenges such as differences in opinion, limited funding and resources, and limited training and collaborative experience. Despite these challenges, they also reported receiving support from school administrators, families/caregivers, mental health professionals, and collaborating agencies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".