How do respondents of primary care surveys compare to typical users of primary care? A comparison of two surveys
Bibliographic record
Abstract
Abstract Background Primary care surveys are a key source of evaluative data; understanding how survey respondents compare to the intended population is important to understand results in context. The objective of this study was to examine the physician and patient representativeness of two primary care surveys (TRANSFORMATION and QUALICOPC) that each used different sampling and recruitment techniques. Methods We linked the physician and patient participants of the two surveys to health administrative databases. Patients were compared to other patients visiting the practice on the same day and other randomly selected dates using sociodemographic data, chronic disease diagnosis, and health system utilization. Physicians were compared to other physicians in the same practice, and other physicians in the intended geographic area using sociodemographic and practice characteristics. Results Physician respondents of the TRANSFORMATION survey included more males compared to their practice groups, but not to other physicians in the area. TRANSFORMATION physicians cared for a larger roster of patients than other physicians in the area. Patient respondents of the QUALICOPC survey did not have meaningful differences from other patients who visit the practice. Patient respondents of the TRANSFORMATION survey resided in more rural areas, had less chronic disease, and had lower use of health services than other patients visiting the practice. Conclusion Differences in survey recruitment methods at the physician and patient level may help to explain some of the differences in representativeness. When conducting primary care surveys, investigators should consider diverse methods of ensuring representativeness to limit the potential for nonresponse bias.
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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 | 0.171 | 0.388 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".