Association between level of compliance with COVID-19 public health measures and depressive symptoms: A cross-sectional survey of young adults in Canada and France
Bibliographic record
Abstract
BACKGROUND: While compliance with preventive measures remains central to limit the spread of COVID-19, these measures critically affected mental health of young adults. We therefore investigated the association between the level of compliance with COVID-19 preventive measures and depressive symptoms among young adults in Canada and France. METHODS: From October to December 2020, we conducted a cross-sectional online survey of young adults ages 18-29 years in Canada (n = 3246) and France (n = 2680) to collect demographic data, experiences with COVID-19 preventive measures, and mental health. Depressive symptoms were assessed by the Patient Health Questionnaire-9 (PHQ-9). Compliance profiles were built using cluster analysis. Weighted multivariable logistic regression was used to estimate associations between compliance level and major depressive symptoms (PHQ-9 score≥15) in each country. RESULTS: One third of respondents reported major depressive symptoms (Canada: 36.4%, France: 23.4%). Four compliance profiles were identified: high (42.5%), medium-high (21.7%), medium-low (18.1%), and low (17.7%), with high levels more frequently observed in Canada compared to France. In both countries, participants in low compliance profile (Canada: Adjusted Odds Ratio (AOR) [95% Confidence Interval] 0.75 [0.58, 0.98], France: AOR 0.60 [0.46, 0.75]), in the medium-low (Canada: AOR 0.58 [0.48, 0.72], France: AOR 0.81 [0.66, 1.01]), and medium-high compliance profiles (Canada: AOR 0.78 [0.65, 0.93], France: AOR 0.77 [0.63, 0.93]) were less likely to report major depressive symptoms compared to the high compliance profile. Ethno-racial minorities, sexual and gender minority, and unemployed young adults had higher odds of reporting such symptoms. CONCLUSIONS: Major depressive symptoms were associated with high compliance with COVID-19 preventive measures among young adults. The implementation of socially-isolating measures should be coupled with mental health interventions to address mental health needs of young adults, with enhanced supports for sub-groups who are structurally disadvantaged (e.g., racialized, unemployed, sexual and gender minority).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".