The effects of age on quality of mental health during the COVID-19 pandemic
Bibliographic record
Abstract
Mental health plays a crucial role in the health and well-being of an individual. Although it is becoming an increasingly prominent topic today, the relationship between age and measures of mental health, such as the frequency of mental illness or perceived mental health, has not been formally studied. Over the last 50 years, the median age of Canadians increased, so studying the impacts of increased age on mental health is important. Furthermore, understanding the impacts of age on mental health disorders is crucial for targeting support to correct populations and challenging associated stigmas. We hypothesized that older age groups would have a lower incidence of mental health disorders and a higher rate of people reporting fair or poor perceived mental health because of increased emotional stability and maturity. Using the Survey on COVID-19 and Mental Health by the government of Canada, we investigated the prevalence of two mental health disorders, generalized anxiety disorder (GAD) and major depressive disorder (MDD) to examine the incidence of mental health disorders across four age brackets. From the Canadian Community Health Survey, data on the perceived mental health of Canadians during the pandemic was compared across age groups. We found significant increase in the prevalence of GAD and MDD in the younger age groups. We also found significant negative correlations between the prevalence of mental health disorders and age using linear regressions. These data suggest that more attention should be placed on the mental well-being of younger adults in Canada.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".