Mental Health Associations with Academic Performance and Education Behaviors in Canadian Secondary School Students
Bibliographic record
Abstract
Course grades, as an indicator of academic performance, are a primary academic concern at the secondary school level and have been associated with various aspects of mental health status. The purpose of this study is to simultaneously assess whether symptoms of mental illness (depression and anxiety) and mental well-being (psychosocial well-being) are associated with self-reported grades (in their primary language [English or French] and math courses) and education behaviors (school days missed due to health, truancy, and frequency of incomplete homework) in a sample of secondary school students across Canada ( n = 57,394). Multivariate imputation by chained equations and multilevel proportional odds logistic regressions were used to assess associations between mental health scores, academic performance and education behaviors. Lower depression and higher psychosocial well-being scores were associated with better grade levels in both math and language courses, as well as better education behaviors. In turn, better education behaviors were associated with higher course grades. Depression scores and psychosocial well-being scores remained associated with higher grades after controlling for education behaviors, however the magnitude of association was diminished. Results indicate that the effects of mental health factors were partially attenuated by education behaviors, suggesting while reduced class attendance and poor homework adherence were associated with both academic outcomes and mental health, they do not account entirely for the association between lower grades and worse mental health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".