Difficulties accessing health care in Canada during the COVID-19 pandemic: Comparing individuals with and without chronic conditions
Bibliographic record
Abstract
Background: Individuals with chronic conditions have higher levels of health care usage and may be at higher risk of more severe outcomes from COVID-19. Therefore, they may have experienced greater difficulty accessing health care during the pandemic because of restrictions on health care services. Data and methods: Data from the Survey on Access to Health Care and Pharmaceuticals During the Pandemic were used to estimate the proportion of individuals in Canada, with and without chronic conditions, who experienced difficulties accessing health care services during the pandemic. Multivariate analyses examined associations between demographic, socioeconomic and health characteristics and the likelihood of experiencing difficulties accessing health care during the pandemic. Results: Nearly one-third (32.0%) of individuals who self-reported having one or more chronic conditions and 24.2% of those who reported no conditions had one or more medical appointments cancelled, rescheduled or delayed because of COVID-19. Smaller proportions of individuals with (19.5%) and without (16.8%) chronic conditions delayed contacting a medical professional because of fear of exposure to COVID-19 in health care settings. Individuals who were younger or had a disability were also more likely than older individuals or those without a disability, respectively, to have had a medical appointment cancelled, rescheduled or delayed because of the pandemic. Women, immigrants, and individuals with multiple chronic conditions were more likely than their counterparts (men, Canadian-born individuals, and individuals with no chronic conditions, respectively) to have delayed contacting a medical professional because of fear of exposure to COVID-19. Interpretation: Individuals with chronic conditions were more likely than those with no chronic conditions to have experienced difficulties accessing health care during the pandemic. Consequently, these individiuals may be at greater risk of experiencing health challenges in the future.
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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 | 0.003 |
| 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 | 0.001 | 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".