Does Continuity of Care Matter in a Universally Insured Population?
Bibliographic record
Abstract
Objective.To examine the relation between continuity of care and preventive health care and emergency department (ED) use in a universal health care system. Data Sources/Study Setting.Administrative data that capture health care use of the entire population of a midwestern Canadian city. Study Design.A population‐based, retrospective study of all individuals who had a least one physician contact in 1998 or 1999 (totalN=536,893). Methods.Logistic regressions were conducted to examine the relation between continuity of care, defined in terms of the proportion of total visits to family physicians (FPs) made to the same FP, and cervical cancer screening, breast cancer screening, influenza vaccination, pneumococcal vaccination, and ED visits, controlling for demographic variables, socioeconomic status (defined in terms of relative affluence of neighborhood of residence), and health status. Principal Findings.Continuity of care was related to better preventive health care and reduced ED use. A consistent socioeconomic gradient also emerged. For instance, the odds of having a mammogram was double for individuals living in the wealthiest neighborhoods, relative to those in the poorest neighborhoods (adjusted odds ratio=2.31, 99 percent CI 2.13–2.50). Conclusions.Having a long‐term relationship with a single physician makes a difference even in a universal health care system. Moreover, socioeconomic disparities remain, suggesting the need to target specifically individuals from lower socioeconomic strata for preventive health care.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".