Patient Experience With Resident Versus Staff Physicians: Results From a Cross-Sectional Patient Survey From Canadian Family Medicine Residencies
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We compared experiences of patients who reported usually being seen by a resident with those usually seen by a staff physician. METHODS: We analyzed responses to a patient experience survey distributed at 13 family medicine teaching practices affiliated with the University of Toronto between May and June 2020. We analyzed responses to seven questions pertaining to timely access, continuity, and patient-centeredness. We compared responses between two types of usual primary care clinicians and calculated odds ratios before and after adjustment for patient characteristics. RESULTS: We analyzed data from 6,545 unique surveys; 18.6% reported their usual clinician was a resident physician. Resident patients were more likely to be older, born outside of Canada, report a high school education or less, and report having difficulty making ends meet. Compared to patients of staff physicians, patients of resident physicians had lower odds of being able to see their preferred primary care clinician and lower odds of getting nonurgent care in a reasonable time. They also had lower odds of reporting patient-centered care, but we found no significant differences in whether the time for an urgent appointment was about right or whether accessing care after hours was easy. CONCLUSIONS: In our setting, patients who reported usually seeing resident physicians had worse continuity of care and timeliness for nonurgent care than patients who reported usually seeing staff physicians despite resident patients being older, sicker, and having a lower socioeconomic position. Postgraduate training programs need to test models to support access and continuity for resident patient panels.
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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 | 0.005 |
| 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 | 0.001 |
| 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".