Age and gender differences in coping and mental health during and post COVID-19 lockdown
Bibliographic record
Abstract
Introduction As a reaction to growing number of COVID-19 cases in Quebec, the government issued a lockdown to prevent further spread of the virus in March 2020. The novelty of the imposed restrictions warranted an assessment of adult coping and potential effects on anxiety and depressive symptoms. Objectives The purpose of the present study was to evaluate methods of coping employed during Quebec’s lockdown and their potential ramifications on anxiety and depressive symptoms post-lockdown in Quebec. Methods In a retrospective longitudinal design, two-hundred and twenty-three (n = 223) adults (65.5% female; 34.5% male) completed the study online. They were asked to fill out several questionnaires and provide demographic information. Results Analysis revealed significant improvement in anxiety symptoms post-lockdown relative to during lockdown across the entire sample. Depressive symptoms also improved significantly across the sample, but the difference was less pronounced among 18–34-year-olds than those 35 and above. Male adults aged 18-34 utilized maladaptive coping strategies to the greatest extent. Moreover, maladaptive coping was significantly associated with anxiety and depressive symptoms and predicted depressive symptoms post-lockdown. Further investigation revealed that young adult males differed from females in their use of substances and self-blame to cope. Conclusions Overall, the data suggest that the lockdown adversely affected anxiety and depressive symptoms among the general population. Furthermore, young adults, particularly males, were most susceptible to depressive symptomatology due in part to their methods of coping with the novel context. A follow-up study is warranted. Future studies should also seek to recruit individuals whose self-identified gender is non-traditional (e.g., non-binary). Disclosure No significant relationships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".