Bibliographic record
Abstract
The essence of primary care reform is the creation of a formal relationship between a physician provider and a patient.The idea is that a patient will sign on with a particular provider who will then take medical responsibility for accessibility, quality of care and continuity of care.However, various aspects of physician remuneration, specifically negation, are perceived as a disincentive for physicians.Negation refers to the deduction of a specified amount from the contracted physician if a patient sees another family physician or general practitioner not associated with their practice.How often and to what extent patients see different family physicians or general practitioners (FPs/GPs) becomes an important question in the debate.Scientists at ICES are examining several aspects of family medicine and general practice, including the use of physician claims from the Ontario Health Insurance Plan (OHIP), to learn how often patients see different primary care physicians.In one study, a random sample of 538 primary care physicians, having more than 500 OHIP claims in fiscal 1997/98, was examined.Only office-based visits were included in the study.This group of physicians saw 738,910 patients over 6,493,345 visits.In one year, about 31.0% of patients saw one FP/GP, 29.8% saw two FPs/GPs, 18.3% saw three, 10.0% saw four and 10.9% saw five or more FPs/GPs.When specialist consultation visits were included, the proportion of patients seeing only one physician dropped to 17.7% while the proportion of patients visiting more than five physicians a year grew to 26.0%.Using OHIP consultation data, patients were "assigned" to various FP/GP practices using different assignment rules.For the "75% Rule," a patient was assigned to a primary care physician if at least 75% of his or her visits were to this primary care physician.The "Majority Rule" assigned a patient to a primary care physician if at least 50% of visits were to this primary care physician.The "Plurality Rule" assigned the patient to the primary care physician who accounted for the highest proportion of total visits made by the patient.In Table 1, the Majority Rule and Plurality Rule assigned the most patients to the randomly selected FP/GP physician (53.3% and 56.7% respectively), with the plurality rule also assigning more patients to other FPs/GPs (29.2%).The 75% Rule had the most
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".