A cross-sectional survey of activities to support mental wellness during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, public health restrictions such as social distancing, isolation and self-quarantine have been implemented for several months. Because of these restrictions, in-person contact with friends, family, and mental health supports had been limited, potentially impacting mental wellbeing. OBJECTIVES: In this study, we examined the impact of the pandemic on the mental health of adults and investigated the types of activities people engage in to manage and maintain their mental health. METHODS: An online survey was circulated in Canada and had a total of 221 participants from September 24 to December 8, 2020. RESULTS: The majority of participants were females (73.2%), between the ages of 18 and 34 (51.1%), and employed full-time (56.1%). Individuals who are unemployed and those with an annual income less than $25,000 had the highest scores in depression, anxiety and psychological distress. Around 19.4% of the sample scored above the cutpoint for depression, which is higher compared to a pre-pandemic population prevalence of 4.7%. Similarly, higher prevalence of anxiety and distress symptoms were observed: 16.3% of the sample had moderate anxiety symptoms compared to a pre-pandemic population prevalence of 11.6%; and 37.7% of the sample had moderate distress symptoms compared to a pre-pandemic population prevalence of 20%. CONCLUSIONS: Our findings suggest that the COVID-19 pandemic has negatively impacted the mental health of many adults and that individuals engage in a wide range of activities that may maintain and promote mental wellness during the pandemic, such as exercising, reading, and listening to music.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".