Classifying Physician Practice Style
Bibliographic record
Abstract
BACKGROUND: Primary medical care is changing-more female providers, desire for better work-life balance, and increasing availability of walk-in clinics have altered service delivery. There is no uniform physician practice style, and understanding service availability and delivery requires analysis of family physicians' practice patterns, rather than just physician counts. METHODS: This paper offers a new approach for describing the practice habits of primary care physicians. We use administrative data to identify activities associated with acting as "most responsible" physicians. We used British Columbia's administrative health care data from 2007/2008 to 2011/2012 to derive information regarding physicians, patients, and service delivery. We developed 5 variables to describe practice style: referrals, oversight, screening, initial prescribing for long-term medications, and repeat visits. Cluster analysis revealed 3 distinct groups of physicians. RESULTS: Only 24% of the primary care physicians were assigned to the high-responsibility group, whereas 36% and 39% were in the low-responsibility and mixed-practice groups, respectively. All cluster variables follow a similar pattern, with the high-responsibility and low-responsibility physicians many multiples apart on the means and the mixed group falling in between. Several forms of sensitivity analysis confirmed the robustness of these results. CONCLUSIONS: Physician practice patterns influence the effective supply of primary care. The fact that more than one third of British Columbia physicians are identified as "low responsibility" has implications for the delivery of primary care, both in ensuring that people have access to regular care and in insuring high-quality and comprehensive care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".