Symptoms of depression and anxiety, and unmet healthcare needs in adults during the COVID-19 pandemic: a cross-sectional study from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disrupted access to healthcare services in Canada. Research prior to the pandemic has found that depression and anxiety symptoms were associated with increased unmet healthcare needs. The primary objective of this study was to examine if mental health was associated with perceived access to healthcare during the pandemic METHODS: A cross-sectional study was conducted using data from 23,972 participants (aged 50-96) in the Canadian Longitudinal Study on Aging COVID-19 Exit Survey (Sept-Dec 2020). We used logistic regression to estimate how the presence of depression and anxiety symptoms, defined using scores of ≥10 on the Center for Epidemiologic Studies Depression Scale and ≥10 on the Generalized Anxiety Disorder Scale, were associated with the odds of reporting: 1) challenges accessing healthcare, 2) not going to a hospital or seeing a doctor when needed, 3) experiencing barriers to COVID-19 testing. Models were adjusted for sex, age, region, urban/rural residence, racial background, immigrant status, income, marital status, work status, chronic conditions, and pre-pandemic unmet needs. RESULTS: The presence of depressive (aOR=1.96; 95% CI=1.82, 2.11) and anxiety symptoms (aOR=2.33; 95% CI=2.04, 2.66) compared to the absence of these symptoms were independently associated with higher odds of challenges accessing healthcare. A statistically significant interaction with sex suggested stronger associations in females with anxiety. Symptoms of depression (aOR=2.88; 95% CI=2.58, 3.21) and anxiety (aOR=3.05; 95% CI=2.58, 3.60) were also associated with increased odds of not going to a hospital or seeing a doctor when needed. Lastly, depressive (aOR=1.99; 95% CI=1.71, 2.31) and anxiety symptoms (aOR=2.01; 95% CI=1.58, 2.56) were associated with higher odds of reporting barriers to COVID-19 testing. There was no significantly significant interaction with sex for the latter two outcomes. CONCLUSION: The presence of depression and anxiety symptoms were strongly associated with perceived unmet healthcare needs during the COVID-19 pandemic. Interventions to improve healthcare access for adults with depression and anxiety during the pandemic may be necessary.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".