Population Patterns of Chronic Health Conditions, Co-morbidity and Healthcare Use in Canada: Implications for Policy and Practice
Bibliographic record
Abstract
Managing chronic health conditions is a daily reality for approximately nine million Canadians, and the numbers of people affected are expected to increase as our population ages, particularly if risk factors that contribute to poor health continue to rise. These conditions impact health and well-being and represent a significant, and growing, healthcare and economic burden. The Health Council of Canada has focused its attention on the prevention and management of chronic conditions to encourage discussion of the changes to public policy, healthcare management and health services delivery required to improve health outcomes for Canadians. In December 2007, the Health Council released a report that described the health and healthcare use among Canadians who have chronic conditions as well as their self- reported experiences with chronic illness care. It highlighted initiatives under way in all jurisdictions to improve the situation. In order to inform that report, we analyzed population-based survey data from the Canadian Community Health Survey to report on patterns of health and healthcare use by community-dwelling youth and adults who have one or more of seven high-prevalence, high-impact chronic conditions. We demonstrated that the vast majority of people with chronic conditions have a regular medical doctor and visit community-based doctors and nurses frequently. Not surprisingly, people with chronic conditions use healthcare services more often and more intensively than do those without, and the intensity of service use increases as the numbers of conditions go up. The 33% of Canadians with one or more of seven chronic conditions account for approximately 51% of family physician/general practitioner consultations, 55% of specialist consultations, 66% of nursing consultations and 72% of nights spent in a hospital. This information highlights the imperative of immediate, comprehensive and sustained attention to undertake proven strategies to delay or prevent the onset of chronic conditions and to improve the quality of primary healthcare to prevent complications, reduce the need for more expensive health services and secure a better quality of life for Canadians.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".