Describing Unmet Healthcare Needs During the COVID-19 Pandemic: an Analysis of the Canadian Longitudinal Study on Aging (CLSA) COVID-19 Questionnaire Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic disrupted access to healthcare services in Canada, but little is known about the magnitude of unmet healthcare needs and characteristics associated with increased risk of unmet needs in the adult population. Objectives: First, to describe unmet healthcare needs, including COVID-19 testing access, and to evaluate the association of the social determinants of health (SDOH) and chronic conditions with unmet healthcare needs. Secondly, to evaluate the association between symptoms of depression and anxiety with unmet healthcare needs, and test if the interaction was modified by sex. Methods: The data of 23,972 adults who completed the Canadian Longitudinal Study on Aging COVID-19 Questionnaire Study exit survey (Sept.–Dec. 2020) was analyzed. Three outcomes were evaluated: 1) challenges accessing healthcare, 2) not going to a hospital or seeing a doctor when needed, 3) experiencing barriers to COVID-19 testing. For objective 1, a prospective cohort study was conducted. For objective 2, a cross-sectional study was conducted. RESULTS: Overall, 25% of adults in Canada reported challenges accessing healthcare, 8% did not go to a hospital or see a doctor when needed, and 4% experienced barriers to COVID-19 testing. Several SDOH, including sex, immigrant status, racial background, education and income, were associated with unmet needs. The odds of reporting all three outcomes declined with age. Pre-pandemic unmet needs were strongly associated with higher odds of all three outcomes, while the presence of chronic conditions was associated with higher odds of the first two outcomes. Symptoms of depression and anxiety were strongly associated with all three outcomes. Interaction with sex was found for the first outcome, with stronger associations in females. Conclusions: This thesis identified groups that experienced difficulties accessing healthcare services during the pandemic. Future research may assess consequences of unmet needs, evaluate mechanisms that cause unmet needs and determine ideal interventions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".