Current Approaches to Research on Mental Health and Learning
Bibliographic record
Abstract
One in five students in Canada will struggle with mental health challenges that interfere with their academic and social functioning (Waddle, McEwan, Shepherd, Offord, & Hua, 2005). Growing awareness of the role mental health has on academic achievement is evidenced by diverse methods researching mental health in educational environments ranging from kindergarten to post-secondary. Papers in this structured-poster symposium session profile the range of research on mental health and learning occurring across Canada in elementary, secondary, and post-secondary contexts: high school students experiencing and mitigating anxiety [Paper 1], university students using self-regulated learning to optimize their mental health around academic challenges and tasks [Paper 2], and developing teacher social-emotional competencies and the role these competencies have on student social-emotional well-being and mental health broadly [Paper 3].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.016 | 0.050 |
| Scholarly communication | 0.030 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".