Stronger together: Coping behaviours and mental health changes of Canadian adolescents in early phases of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The impact of the COVID-19 pandemic and consequent public health restrictions on the mental health of adolescents is of global concern. The purpose of this study was to examine how Canadian adolescents coped during the early pandemic and whether different coping methods were associated with changes in mental health from before the pandemic to the early lockdown response. METHODS: Using two-year linked survey data (2018-2020) from a prospective cohort of secondary school students (n = 3,577), linear regression models were used to examine whether changes in mental health (anxiety [Generalized Anxiety Disorder-7 scale], depression [Center for Epidemiologic Studies Depression 10-item scale Revised], emotion regulation [Difficulties in Emotion Regulation Scale], psychosocial well-being [Flourishing scale]) were related to each coping behaviour. RESULTS: The most common reported coping behaviours included staying connected with friends online (78.8%), playing video games, watching TV/movies, and/or surfing the internet/social media (76.2%), studying or working on schoolwork (71.0%), and getting exercise (65.2%). The use of positive coping mechanisms during the early pandemic period (e.g., keeping a regular schedule, time with family, time with friends online) was associated with less adverse mental health changes from before to during the early lockdown; whereas, negative coping mechanisms (e.g., spending time alone, eating junk food) were consistently associated with more adverse mental health changes. CONCLUSION: This study demonstrates the importance of social support and connections with both friends and family, as well as keeping and maintaining a routine, over the pandemic. Interventions supporting positive relationships and engagement in these coping behaviours may be protective for adolescent mental health during disruptive events.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".