Neighbourhood influences on youth mental health and stress levels during the first six months of the COVID-19 pandemic
Bibliographic record
Abstract
The impacts of the COVID-19 pandemic on youth mental health and stress levels warrants urgent attention. In Canada, as elsewhere in the world, public health measures in the early stages of the pandemic dramatically transformed the everyday geographies of young people. In the hyper-localisation of everyday life, surrounding neighbourhood features like parks and food-related stores may have provided the only outlets for physical activity, social interaction, and relaxation outside of the home. We examine how health-related behaviours, neighbourhood features, and demographic factors may relate to changes in youth mental health and stress levels during the first six months of the pandemic. A cross-sectional youth-informed online survey was conducted with youth, aged 13–19, in London, Ontario, Canada. Respondents were surveyed about their mental health and stress levels before and during the first six months of the COVID-19 pandemic. From 279 respondents, we identified how age, gender, ethnicity, dietary habits, physical activity levels, and availability of parks, fast food, convenience stores and grocery stores could correlate with mental health and stress levels. Given the role played by public spaces, our work underscores the importance of including youth perspectives in the planning of the public realm which contributes to healthy and thriving communities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".