Findings from An Online Survey on the Mental Health Effects of COVID-19 on Canadians with Disabilities and Chronic Health Conditions
Bibliographic record
Abstract
Although the COVID-19 pandemic has led to worsening mental health outcomes throughout the Canadian population, its effects have been more acute among already marginalized groups, including people with disabilities and chronic health conditions. This paper examines how heightened fears of contracting the virus, financial impacts, and social isolation contribute to declining mental health among this already vulnerable group. This paper investigates how increases in anxiety, stress, and despair are associated with concerns about getting infected, COVID-19-induced financial hardship, and increased social isolation as a result of adhering to protective measures among people with disabilities and chronic health conditions. This study uses original national quota-based online survey data (n=1, 027) collected in June 2020 from people with disabilities and chronic health conditions. Three logistic regression models investigate the relationship between COVID-19's effects on finances, concerns about contracting the virus, changes in loneliness and belonging, and measures taken to combat the spread of COVID-19 and reports of increased anxiety, stress, and despair, net of covariates. Models show that increased anxiety, stress, and despair were associated with negative financial effects of COVID-19, greater concerns about contracting COVID-19, increased loneliness, and decreased feelings of belonging. Net of other covariates, increased measures taken to combat COVID-19 was not significantly associated with mental health outcomes. Findings address how the global health crisis is contributing to declining mental health status through heightened concerns over contracting the virus, increases in economic insecurity, and growing social isolation, speaking to how health pandemics exacerbate health inequalities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".