The psychological effects of COVID-19 spread in young Canadian adults
Bibliographic record
Abstract
Background and aim:The novel coronavirus disease 2019 (COVID-19) not only threatens physical health but also psychological well-being. As a result of the pandemic, increased mental health burdens have been recorded in young adults around the world. We sought to understand the association of stressors related to the COVID-19 pandemic with symptoms of psychological and emotional distress in young Canadian adults.Method:Questionnaire respondents were asked about the extent to which they were personally impacted by COVID-19, and symptoms related to depression, anxiety, post-traumatic disorder, and emotional distress.Results:Of 84 respondents, most identified as female (74%; 62/84). Overall, 61% (51/84) reported experiencing symptoms of psychological distress related to depression, anxiety, or post-traumatic stress disorder (PTSD); specifically, 43% (36/84) reported anxiety-related symptoms, 33% (28/84) reported depression-related symptoms, and 6% (5/84) reported PTSD-related symptoms. Individuals with family in settings high risk for COVID-19 infection and individuals who received government aid with a reduction in income were 4.30-fold and 2.80-fold more likely, respectively, to experience symptoms of psychological distress (95% CI 1.31–14.14; p = .013 and 95% CI 1.08–7.25; p = .038, respectively). Visits to social media daily to hourly for COVID-19 related news resulted in a 3.24-fold increase in the likelihood of experiencing depression-related symptoms (95% CI 1.26–8.35; p = .020).Conclusion:We demonstrate a necessity to focus on alleviating the mental health burdens and contributing stressors within young Canadian adults. Though the mental health burdens are currently exacerbated by the effects of the COVID-19 pandemic, they may persist after the pandemic ends if left unaddressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".