“We are at risk too”: The disparate impacts of the pandemic on younger generations
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has resulted in profound global impact with high rates of morbidity and mortality. It is essential to understand the psychosocial impacts of the pandemic to identify appropriate prevention and intervention targets. Across generational groups, this study examined: (1) rates of precautions and adaptive and maladaptive health behaviours, (2) differences in levels of anxiety, and (3) rates of and changes in COVID-related concerns over time during the early outbreak of COVID-19 in Canada. Methods We analyzed data from two Canadian population-based datasets: the Canadian Perspective Survey Series: Impact of COVID-19 survey ( N =4,627; March 29-April 3, 2020), and Crowdsourcing: Impacts of COVID-19 on Canadians – Your Mental Health ( N =45,989; April 24-May 11, 2020). We categorized generational age group, participants self-reported changes in behaviours and COVID-related concerns, and a validated measure assessed anxiety symptoms. Results There are age differences in behavioural responses to the pandemic; adaptive health habits (e.g., exercise) were stable across groups, while maladaptive health habits (e.g., substance use) were highest among younger groups. COVID-related precautions were also highest among the younger generations, with Generation X exhibiting the highest rate of precautionary behaviour. Results also revealed that anxiety and worry are prevalent in response to the pandemic across all generations, with the highest rate of clinically significant anxiety among Millennials (36.0%). Finally, COVID-related concerns are greatest for younger generations and appear to be decreasing with time. 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".