Representativeness of patients and providers in the Canadian Primary Care Sentinel Surveillance Network: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) has established a national repository of primary care patient health data that is used for both surveillance and research. Our main objective was to determine how representative the data for patients and primary care practitioners in the CPCSSN are when compared with the Canadian population. METHODS: In this cross-sectional study, we compared the 2013 CPCSSN patient sample with age and sex information from the 2011 census. The CPCSSN provider sample in 2013 was compared with the 2013 National Physician Survey. Results were stratified by 5 clinically relevant age categories and sex, and male-to-female ratios were calculated. RESULTS: Patients who were 65 years of age and older represented 20.4% of the CPCSSN sample but only represented 14.8% of the Canadian population (2011 census). Among young adults (20-39 yr), 39.3% fewer men than women visited their primary care practitioner within 2 years. CPCSSN sample practitioners were more likely to be under 45 years of age, more likely to be female and more likely to be in an academic practice. INTERPRETATION: It is important to consider adjusting for age and sex when using CPCSSN data. CPCSSN practitioners are likely not representative of family physicians; therefore, CPCSSN needs to recruit more nonacademic practices, community clinics and practices that have a larger proportion of male providers.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".