Promoting mental well-being: educators’ perceptions of mental well-being practices and programs for including all students
Bibliographic record
Abstract
The Canadian Mental Health Organization states that there is a substantial percentage of children and youth in the school age range who suffer from some form of mental health disorder (Canadian Mental Health Organization, 2020). A troubling finding is that only twenty percent of individuals who suffer from a mental health disorder receive adequate support (Canadian Mental Health Organization, 2020). Schools have been regarded as important environments for promoting the well-being of children, as they spend over 6 hours a day and more than 180 days a year in school (Stewart et al., 2004). In the school year 2020/2021, Manitoba children had 196 scheduled days of school (Education, n.d.). The promotion of well-being for students includes responding efficiently to all student needs and learning challenges. In this fast-paced world, educators are looking for strategies to assist students and help them thrive. This qualitative study examined six elementary educators' (e.g., classroom teachers, support teachers and administrators) perceptions of practices and programs intended to promote students' mental wellbeing in Manitoba, Canada, including interventions designed for those who lack self-regulation or present with exceptional social and emotional needs. Each participant was individually interviewed using a digital platform. The data was analyzed thematically. The findings support a need for more evidenced based resources and support, a need for a school wide framework, more resources and supports as well as more focused measuring of outcomes. The implications of promoting mental well-being in schools for all students as well as areas for future research are discussed.
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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 | 0.004 | 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 | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".