Positive mental health and perceived change in mental health among adults in Canada during the second wave of the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Canadian surveys from spring/summer 2020 suggest the prevalence of some positive mental health (PMH) outcomes have declined compared to pre-pandemic levels. However, less is known about the state of PMH during the second wave of the COVID-19 pandemic. METHODS: We compared adults' self-rated mental health (SRMH), community belonging and life satisfaction in Fall 2020 versus 2019 in the overall population and across sociodemographic characteristics using cross-sectional data from the Survey on COVID-19 and Mental Health (September-December, 2020) and the 2019 Canadian Community Health Survey. We also conducted regression analyses to examine which sociodemographic factors were associated with reporting in Fall 2020 that one's mental health was about the same or better compared to before the pandemic. RESULTS: Fewer adults reported high SRMH in Fall 2020 (59.95%) than in 2019 (66.71%) and fewer reported high community belonging in Fall 2020 (63.64%) than in 2019 (68.42%). Rated from 0 (very dissatisfied) to 10 (very satisfied), average life satisfaction was lower in Fall 2020 (7.19) than in 2019 (8.08). Females, those aged under 65 years, those living in a population centre, and those absent from work due to COVID-19 had lower odds of reporting that their mental health was about the same or better in Fall 2020. CONCLUSION: The PMH of adults was lower during the pandemic's second wave. However, the majority of individuals still reported high SRMH and community belonging. The findings identify certain sociodemographic groups whose mental health appears to have been more negatively impacted by the pandemic. Continued surveillance is important in ensuring mental health builds back better and stronger in Canada after the 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".