Sex-specific estimates of positive mental health among youth before and during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
INTRODUCTION: Positive mental health (PMH) is an essential component of mental health and well-being. While population-level data show a decrease in youth PMH during the COVID-19 pandemic, there are sex differences that have not been examined. METHODS: Data from the 2017, 2019 and 2021 Canadian Community Health Survey were used to examine youth (12-17 years) PMH before and during the COVID-19 pandemic. Sex-specific prevalence of high self-rated mental health (SRMH) and average life satisfaction (LS) for each year were calculated and disaggregated by sociodemographic characteristics. Differences between years were quantified, and statistical significance was determined using t tests (p value < 0.004 after Bonferroni correction). RESULTS: From 2019 to 2021, there were significant decreases in the prevalence of high SRMH (from 66.4% to 52.3%) and average LS (8.7 to 8.2) among female youth, at the overall level and across the majority of sociodemographic groups. As for males, no significant decreases were seen at the overall level. After disaggregation, a significant decrease in prevalence of high SRMH was observed from 2019 to 2021 among male youth living in Quebec and nonimmigrant male youth. There were no significant changes in the prevalence of high SRMH or average LS from 2017 to 2019. The sex-specific differences in PMH varied across sociodemographic characteristics. CONCLUSION: The PMH of female youth appears to have been affected during the COVID-19 pandemic more than that of male youth. There were sex-specific differences in PMH across sociodemographic groups, suggesting that not all youth were equally affected. Ongoing surveillance with an intersectional lens is needed to better inform public health strategies.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".