“We Are at Risk Too”: The Disparate Mental Health Impacts of the Pandemic on Younger Generations: Nous Sommes Aussi à Risque: Les Effets Disparates de la Pandémie Sur la Santé Mentale des Générations Plus Jeunes
Bibliographic record
Abstract
OBJECTIVES: The coronavirus 2019 (COVID-19) pandemic has resulted in profound global impact, with older adults at greater risk of serious physical health outcomes. It is essential to also understand generational differences in psychosocial impacts to identify appropriate prevention and intervention targets. Across generational groups, this study examined: (1) rates of precautions and adaptive and maladaptive health behaviors, (2) differences in levels of anxiety, and (3) rates of COVID-related concerns during Wave 1 of COVID-19 in Canada. PARTICIPANTS: = 45,989; April 24 to May 11, 2020). MEASURES: We categorized generational age group. Participants self-reported changes in behaviors and COVID-related concerns, and a validated measure assessed anxiety symptoms. RESULTS: There are generational differences in behavioral responses to the pandemic. Adaptive health habits (e.g., exercise) were comparable across groups, while changes in maladaptive health habits (e.g., substance use) were highest among younger age groups, particularly Millennials (15 to 34 years old). COVID-related precautions were also highest among the younger generations, with Generation X (35 to 54 years old) exhibiting the highest rate of precautionary behavior. Results also revealed that the highest rate of clinically significant anxiety is among Millennials (36.0%; severe anxiety = 15.7%), and the younger generations have the highest rates of COVID-related concerns. CONCLUSION: These early data are essential in understanding at-risk groups given the unpredictable nature of the pandemic and its potential long-term implications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".