Fees for uninsured services: a cross-sectional survey of Ontario family physicians
Bibliographic record
Abstract
BACKGROUND: In Canada, family physicians are permitted to charge patient fees for administrative services that are not covered by the public health insurance program, such as prescription renewals outside of an office visit, and completion of forms and sick notes. The objective of this study was to estimate the proportion of Ontario family physicians who offer various fee structures (i.e., à la carte, annual block fees for all uninsured services rendered or no charge) for uninsured administrative services. METHODS: This was a cross-sectional telephone survey conducted from April to July 2019 of a random sample of family physicians licensed to practise in Ontario. We excluded physicians with missing contact information or additional specialties, or whose primary practice was outside of Ontario, with a walk-in clinic, with an emergency department, or with an organization that cared for a specific population (e.g., nursing home) or did not provide care (e.g., insurance company). We categorized the geographic location of practices as large urban centre (population > 100 000), small to medium centre (population 1000-99 999) or rural area. We calculated survey weights to account for nonresponse and to ensure representativeness of the sample by geographic area and payment model. RESULTS: Among the 221 physicians who met the inclusion criteria, the telephone was not answered at 42 practices, and the contact information was incorrect for 13, resulting in a sample of 166 physicians (response rate 75.1%). The majority of practices reported that they charged fees for uninsured services: 97 (58.3%, 95% confidence interval [CI] 50.6-65.8) charged à la carte, and 33 (20.3%, 95% CI 14.8-27.3) offered patients the option to pay an annual block fee; 19 (11.4%, 95% CI 7.4-17.3) charged no fees. Fee structures varied by geographic area but not physician payment model. INTERPRETATION: The use of à la carte and annual block fees for uninsured administrative services was commonly reported by a sample of Ontario family physicians. Further research is needed to examine the prevalence of patient payment of fees for uninsured services, patient and physician perceptions of fees, and concordance with regulatory guidance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".