An Algorithm Using Administrative Data to Identify Patient Attachment to a Family Physician
Bibliographic record
Abstract
Background. Commonly self-reported questions in population health surveys, such as "do you have a family physician?", represent one of the best-known sources of information about patients' attachment to family physicians. Is it possible to find a proxy for this information in administrative data? Objective. To identify the type of patient attachment to a family physician using administrative data. Methods. Using physician fee-for-service database and patients enrolment registries (Quebec, Canada, 2008-2010), we developed a step-by-step algorithm including three dimensions of the physician-patient relationship: patient enrolment with a physician, complete annual medical examinations (CME), and concentration of visits to a physician. Results. 68.1% of users were attached to a family physician; for 34.4% of them, attachment was defined by enrolment with a physician, for 31.5%, by CME without enrolment, and, for 34.1%, by concentration of visits to a physician without enrolment or CME. Eight types of patient attachment were described. Conclusion. When compared to findings with survey data, our measure comes out as a solid conceptual framework to identify patient attachment to a family physician in administrative databases. This measure could be of great value for physician/patient-based cohort development and impact assessment of different types of patient attachment on health services utilization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".