Access and quality of health care in Canada: Insights from 1998 to the present
Bibliographic record
Abstract
This article reviews perceptions of Canada's public and health professionals regarding access and quality of healthcare. Principal data sources were 13 sequential Health Care in Canada (HCIC) surveys, from 1998 to 2018. Over time, the data series reveals that an increasing majority of the public report receiving quality care, rising from a national average of 53% in 2002 to 61% in 2018. Regionally, the variation in quality care has been relatively narrow, ranging from 52% in the Atlantic and Prairie provinces to 65% in Ontario in 2018. Professionals' ratings for delivery of quality care in 2018 were slightly higher than the public, averaging 65% and ranging from 58% among nurses to 72% and 74% among physicians and administrators. Despite the favourable ratings received for quality of healthcare, a persistent and growing issue in all regions of the country is concern around timely access to care. In 1998, 4% of the public rated prolonged wait times as a concern; in 2018, 43% rated it as their greatest concern. Regionally, the variation in 2018 ranged from 34% in the Atlantic provinces to 49% in Alberta. This concern about timely access involves all major components of healthcare delivery and is anticipated to worsen. Proposals to improve timely access have been suggested, with interdisciplinary, team-based care being the most strongly supported proposal. The Canadian Medicare system is currently recognized as a valued component of our national identity. However, sub-optimal access continues to undermine quality of care. In the absence of improved access, healthcare quality and outcomes will also remain sub-optimal.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".