Office-based procedures among urban and rural family physicians in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To compare FP and GP performance of office-based procedures between urban and rural practices. DESIGN: Descriptive cohort study using health administrative data. SETTING: Ontario. PARTICIPANTS: All FPs and GPs who billed the Ontario Health Insurance Plan for at least 1 office-based procedure between January 1 and December 31, 2006 (N = 8648). MAIN OUTCOME MEASURES: Ontario Health Insurance Plan billings for office-based procedures were adjusted by full-time equivalent (FTE) so that the means are for 1 FTE. Office-based procedures were grouped into 1) surgical procedures, 2) injections and immunizations, 3) electrocardiograms (ECGs), and 4) venipunctures and laboratory tests. The analyses were stratified for FP and GP age, sex, rurality of practice, and participation in a primary care model. RESULTS: There were no substantial differences between FPs and GPs in rural practices compared with those in more urban practices with respect to surgical procedures. Rural FPs and GPs had lower mean numbers of injections and immunizations, ECGs, and venipunctures and laboratory tests than FPs and GPs practising in urban areas. Family physicians and GPs in primary care models had a lower mean number of surgical procedures but a higher adjusted mean number of injections and immunizations, ECGs, and venipunctures and laboratory tests. CONCLUSION: For those procedures that are not dependent on specialist backup or access to more advanced technology, there were no substantial differences between rural and urban FPs and GPs. All comprehensive FPs and GPs should be able to provide these services to their patients. Training programs for all family medicine residents should ensure future FPs and GPs are able to perform these procedures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".