Support for Grieving Students: Ontario Teachers’ Perspectives on Grief Support Resources and Training and how they Affect Preparedness
Bibliographic record
Abstract
The aim of this qualitative research study was to investigate the role of Ontario teachers regarding grieving students, and how prepared teachers feel to meet that role. Data was collected using semi-structured interviews with two fulltime Ontario College of Teachers certified teachers with at least 10 years of experience teaching in the Greater Toronto Area. Participants were asked about what they believe their role is in a grieving student’s life, the resources/training that they sought or that were made available to them, the efficacy of their board, and the overall challenges that teachers face when encountered with a grieving student. Interviews were transcribed and coded to uncover emerging themes consistent across the participants’ responses. The participants reported that they believed teachers played a fundamental role in the lives of grieving students. The participants also reported receiving some training, but it was mostly teacher focused, and not necessarily focused on the students’ well-being. They reported wanting more training to be better prepared for assisting grieving students. Lastly, the participants regarded their administrative teams as somewhat helpful when supporting them in their efforts to help grieving students. As Ontario is currently undergoing a period of increased emphasis on both teacher and student mental health, this research offers insight into the early results of this initiative.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".