Does clinical exposure matter? Pilot assessment of patient visits in an urban family medicine residency program.
Bibliographic record
Abstract
OBJECTIVE: To determine the number of patient visits, patient demographic information, and diagnoses in an urban ambulatory care setting in a family medicine residency program, and assess the correlation between the number of patient visits and residents' in-training examination (ITE) scores. DESIGN: Retrospective analysis of data from resident practice profiles, electronic medical records, and residents' final ITE scores. SETTING: Family medicine teaching unit in a community hospital in Barrie, Ont. PARTICIPANTS: Practice profile data were from family medicine residents enrolled in the program from July 1, 2013, to June 30, 2014, and electronic medical record and ITE data were from those enrolled in the program from July 1, 2010, to June 30, 2015. MAIN OUTCOME MEASURES: Number of patient visits, patient characteristics (eg, sex, age), priority topics addressed in clinic, resident characteristics (eg, age, sex, level of residency), and residents' final ITE scores. RESULTS: Between July 1, 2013, and June 30, 2014, there were 11 115 patient visits. First-year residents had a mean of 5.48 patient visits per clinic, and second-year residents had a mean of 5.98 patient visits per clinic. A Pearson correlation coefficient of 0.68 was found to exist between the number of patients seen and the final ITE scores, with a 10.5% difference in mean score between residents who had 1251 or more visits and those who had 1150 or fewer visits. Three diagnoses (ie, epistaxis, meningitis, and neck pain) deemed important for Certification by the College of Family Physicians of Canada were not seen by any of the residents in clinic. CONCLUSION: There is a moderate correlation between the number of patients seen by residents in ambulatory care and ITE scores in family medicine. It is important to assess patients' demographic information and diagnoses made in resident practices to ensure an adequate clinical experience.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".