Patients report high information coordination between rostered primary care physicians and specialists: A cross-sectional study
Bibliographic record
Abstract
Our study aimed to describe patient experience of information coordination between their primary care physician and specialists and to examine the associations between their experience and their personal and primary care characteristics. We conducted a cross-sectional study of Ontario residents rostered to a primary care physician and visited a specialist physician in the previous 12 months by linking population-based health administrative data to the Health Care Experience Survey collected between 2013 and 2020. We described respondents' sociodemographic and health care utilization characteristics and their experience of information coordination between their primary care physician and specialists. We measured the adjusted association between patient-reported measures of information coordination before and after respondents received care from a specialist physician and their type of primary care model. 1,460 out 20,422 (weighted 7.5%) of the respondents reported that their specialist physician did not have basic medical information about their visit from their primary care physician in the previous 12 months. 2,298 out of 16,442 (weighted 14.9%) of the respondents reported that their primary care physician seemed uninformed about the care they received from the specialist. Females, younger individuals, those with a college or undergraduate level of education, and users of walk-in clinics had a higher likelihood of reporting a lack of information coordination between the primary care and specialist physicians. Only respondents rostered to an enhanced fee-for-service model had a higher odds of reporting that the specialist physician did not have basic medical information about their visit compared to those rostered to a Family Health Team (OR 1.22, 95% Cl 1.12-1.40). We found no significant association between respondent's type of primary care model and that their primary care physician was uninformed about the care received from the specialist physician. In this population-based health study, respondents reported high information coordination between their primary care physician and specialists. Except for respondents rostered to an enhanced fee-for-service model of care, we did not find any difference in information coordination across other primary care models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".