Double Burden of Distress: Exploring the Joint Associations of Loneliness and Financial Strain with Suicidal Ideation During the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
Background: The COVID-19 pandemic, coupled with social distancing measures and economic disruptions, has been associated with increased experiences of loneliness and financial strain. While prior research has examined their separate associations with suicidal ideation, limited attention has been given to their joint relationship. Methods: We used data from the 2022 Mental Health and Access to Care Survey (MHACS) (n = 9861; ages 15+ in Canada) to assess whether financial strain modifies the association between loneliness or emotional distress and suicidal ideation. Multivariable survey-weighted logistic regression was conducted, adjusting for sociodemographic, economic, psychosocial, and health-related characteristics, including mental health and substance use conditions. Results: Among the 9743 respondents who answered the question on suicidal ideation, 355 (3.65%) reported suicidal ideation. Compared to individuals with neither stressor, those who experienced loneliness or emotional distress alone had 1.54 times higher odds of suicidal ideation (aOR = 1.54, 95% CI: 1.29–1.84, p < 0.001), while those who reported financial strain alone had 0.58 times the odds (aOR = 0.58, 95% CI: 0.43–0.80, p = 0.001). The highest odds were observed among individuals who experienced both loneliness/emotional distress and financial strain, with an adjusted odds ratio of 2.05 (95% CI: 1.71–2.45, p < 0.001), indicating an interaction between these stressors. Conclusion: The co-occurrence of loneliness or emotional distress and financial strain was associated with higher odds of suicidal ideation during the COVID-19 pandemic, compared to individuals experiencing neither stressor. These findings highlight the importance of considering both social and economic stressors when assessing mental health risks. Given the cross-sectional nature of this study, further longitudinal research is needed to explore the temporal relationships and potential causal pathways linking these experiences to suicidal ideation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".