Minding the Gap: Factors Associated With Primary Care Coordination of Adults in 11 Countries
Bibliographic record
Abstract
PURPOSE: Care coordination has been identified as a key strategy in improving the effectiveness, safety, and efficiency of the US health care system. Our objective was to determine whether population or health care system issues are associated with primary care coordination gaps in the United States and other high-income countries. METHODS: We analyzed data from the 2013 Commonwealth Fund International Health Policy (IHP) survey with multivariate logistic regression analysis. Respondents were adult primary care patients from 11 countries: Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, United Kingdom, and the United States. Poor primary care coordination was defined as participants reporting at least 3 gaps in the coordination of care out of a maximum of 5. RESULTS: Analyses were based on 13,958 respondents. The rate of poor primary care coordination was 5.2% (724/13,958 respondents) overall and highest in the United States, at 9.8% (137/1,395 respondents). Multivariate regression analysis among all respondents found that they were less likely to experience poor primary care coordination if their primary care physician often or always knew their medical history, spent sufficient time, involved them, and explained things well (odds ratio = 0.6 for each). Poor primary care coordination was more likely to occur among patients with chronic conditions (odds ratios = 1.4-2.1 depending on number) and patients younger than 65 years (odds ratios = 1.6-2.3 depending on age-group). Among US respondents, insurance status, health status, household income, and sex were not associated with poor primary care coordination. CONCLUSIONS: The United States had the highest rate of poor primary care coordination among the 11 high-income countries evaluated. An established relationship with a primary care physician was significantly associated with better care coordination, whereas being chronically ill or younger was associated with poorer care coordination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".