Mental distress and virtual mental health resource use amid the COVID-19 pandemic: Findings from a cross-sectional study in Canada
Bibliographic record
Abstract
Objective: This paper characterizes levels of mental distress among adults living in Canada amid the COVID-19 pandemic and examines the extent of virtual mental health resource use, including reasons for non-use, among adults with moderate to severe distress. Methods: = 3030) in Canada during the pandemic. Levels of mental distress were assessed using the Kessler Psychological Distress Scale. Descriptive statistics were used to examine virtual mental health resource use among participants with moderate to severe distress, including self-reported reasons for non-use. Results: Levels of mental distress were classified as none to low (48.8% of participants), moderate (36.6%), and severe (14.6%). Virtual mental health resource use was endorsed by 14.2% of participants with moderate distress and 32% of those with severe distress. Participants with moderate to severe distress reported a range of reasons for not using virtual mental health resources, including not feeling as though they needed help (37.4%), not thinking the supports would be helpful (26.2%), and preferring in-person supports (23.4%), among other reasons. Conclusions: This study identified a high burden of mental distress among adults in Canada during the COVID-19 pandemic alongside an apparent mismatch between actual and perceived need for support, including through virtual mental health resources. Findings on virtual mental health resource use, and reasons for non-use, offer directions for mental health promotion and health communication related to mental health literacy and the awareness and appropriateness of virtual mental health resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".