Calculating physician supply using a service day method and the income percentiles method: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: It is important to have an accurate count of physicians and a measurable understanding of their service provision for physician resource planning. Our objective was to compare 2 methods (income percentiles [IP] and service day activities [SVD]) for calculating the supply of full-time (FT) and part-time (PT) primary care physicians (PCPs) as measures of both physician supply counts and level of provider continuity. METHODS: Using an observational study design, we compared 2 methods of calculating the supply of PT and FT PCPs for 2011-2015. For the IP approach, the Canadian Institute for Health Information's method was applied to Alberta Health billing data. The SVD method calculated annual service days for fee-for-service PCPs. A simple descriptive analysis was conducted of the supply of PT and FT PCPs. RESULTS: The 2 methods agreed on the FT versus PT status of 85.2% of PCPs in 2015 but disagreed on the status of 490 PCPs. A total of 239 PCPs were classified as working FT by the IP method but PT by the SVD method. Two hundred and fifty-one PCPs were classified as working PT according by the IP method but FT by the SVD method. The former group of 239 PCPs worked fewer days per week (3.22 v. 4.1) and fewer weekend days per year (8.6 v. 24.1), billed more per year ($300 327 v. $201 834) and saw more patients per day (26.8 v. 17.8) with less continuity of care (38.0% v. 72.0%) than the latter group of 251 PCPs. INTERPRETATION: The SVD method provides a valid alternative to calculating GP supply that distinguishes groups of physicians that the standard IP methodology does not. Those groups provide very different service; policy-makers may benefit from distinguishing them.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".