COVID-19 Pandemic: The Impact of COVID-19 on Mental Health and Life Habits in the Canadian Population
Bibliographic record
Abstract
Objectives: The study aims to investigate the rate of clinical depression in the adult population during the COVID-19 pandemic, as well as the changes in anxiety, distress, suicidal ideation, and their relations with several personal and interpersonal/social variables. Methods: This is an epidemiological, non-interventional study. It is part of an international multi-center study, with the main site at the Aristotle University of Thessaloniki, in Greece (COMET-G Study). We are presenting aspects of the research involving the Canadian site, based on 508 Canadian responders to the online survey (QAIRE). Results: Of the 508 responders, 72.2% were females aged 42.57 ± 14.00 years; 27.2% were males aged 42.24 ± 15.49 years; and 0.6% were others aged 46.33 ± 17.79 years. Increased anxiety during the lockdown was reported by 69.3% of those surveyed. The rate of suicidal thoughts increased in 19.5% of participants during the lockdown. Depression was reported by 22% of responders, while distress was present in 18.4%. We found a greater prevalence of depression, but not distress, in individuals with a history of any mental disorder. Based on the multiple regression analysis, we found four CORE factors equally influencing the changes in mental health during the lockdown (gender, quality of sleep, family conflicts, and changes in daily routine). In the Canadian population, two major changes acted as protective factors, significantly expressed when compared with the worldwide tendencies: fewer financial difficulties; and an increase in religious beliefs. Conclusion: The rate of major depression, distress, and suicidal ideation was higher in Canadians than in the worldwide population (per COMET-G), but the relative risk to develop depression in the presence of a history of mental disorders was lower. Almost 90% of Canadians believed in the real story of COVID source of provenience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".