Supporting Students' Mental Health and Well Being in Elementary Education
Bibliographic record
Abstract
Research has shown that in Canada1 in 5 citizens suffer from a mental health problem every year (MHCC, 2012). This statistic does not exclude children, who are the students that fill our elementary classrooms. It has also been concluded through various policy and research documents that there is a direct correlation between student mental health difficulties and academic achievement (SMH-ASSIST, 2013). These statistics therefore optimally position teachers and education professionals to support the reduction of stigma, promote positive mental health, and build student social- emotional skills (SMH-ASSIST, 2013). However statistics also show that teachers do not feel supported or prepared to do this type of work even though they believe it is crucial (TDSB, 2012; Froese-Germain & Riel, 2012; Whitley et. al., 2012; Reinke et. al., 2011; etc.). This qualitative research project uses a literature review and two semi-structured interviews to investigate how a small sample of educators support student mental health and well being in their practices. Interviews with two educators show that teachers are actively working to support mental health and well being in their classrooms however there is still a further need for provisions, education, and a more cohesive implementation plan to support students’ mental health and well being at the elementary level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".