School learning modes during the COVID-19 response and pre- to during pandemic mental health changes in a prospective cohort of Canadian adolescents
Bibliographic record
Abstract
PURPOSE: Considerable debate centered on the impact of school closures and shifts to virtual learning on adolescent mental health during the COVID-19 pandemic. We evaluated whether mental health changes differed by school learning modes during the pandemic response among Canadian adolescents and whether associations varied by gender and perceived home life. METHODS: We used prospective survey data from 7270 adolescents attending 41 Canadian secondary schools. Conditional change linear mixed effects models were used to examine learning mode (virtual optional, virtual mandated, in-person, and blended) as a predictor of change in mental health scores (depression [Centre for Epidemiologic Studies - Depression], anxiety [Generalized Anxiety Disorder-7], and psychosocial well-being [Flourishing scale]), adjusting for baseline mental health and covariates. Gender and home life happiness were tested as moderators. Least square means were calculated across interaction groups. RESULTS: Students learning in a blended learning mode had greater anxiety increases relative to their peers in other learning modes. Females learning fully in-person and males learning virtually when optional reported less of an increase in depression scores relative to their gender counterparts in other learning modes. Learning virtually when optional was associated with greater declines in psychosocial well-being in students without happy home lives relative to other learning modes. CONCLUSION: Findings demonstrate the importance of considering gender and home environments as determinants of mental health over the pandemic response and when considering alternative learning modes. Further research is advised before implementing virtual and blended learning modes. Potential risks and benefits must be weighed in the context of a pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".