Using a Web-based system to monitor practice profiles in primary care residency training.
Bibliographic record
Abstract
OBJECTIVE: To explore the use of Web-based resident practice profiles (RPPs) as a means of tracking the clinical experiences of residents to ensure an adequate educational experience. DESIGN: Quantitative analysis of recorded patient encounters with residents. SETTING: The Department of Family and Community Medicine at St Michael's Hospital in Toronto, Ont. PARTICIPANTS: Twenty-seven residents enrolled in the department's training program between July 1, 2006, and June 30, 2007. MAIN OUTCOME MEASURES: The clinical experiences of residents with respect to patient demographic information, procedures performed, and diagnoses. Resident data were stratified by age, sex, training status, and source of medical degree, and RPPs were compared with patient profiles of physicians at the study site, at the university, and in provincial practices. RESULTS: A total of 9108 patient visits were recorded by the 27 residents during the academic year. Patient visit characteristics were very similar across all the resident variables except with respect to sex. The top 8 diagnoses encountered by residents were very similar to those of the comparison groups; anxiety or neurosis was the most common problem. Injections and Papanicolaou smears were the most common procedures, with 17.9 and 11.6 procedures, respectively, performed on average per resident during the study period. CONCLUSION: The RPP is an excellent Web-based tool to capture the clinical experience of postgraduate trainees. The practice profiles of the resident group were very similar to those of physicians in the study site, the university, and the province, demonstrating that common diagnoses made in practice correlate well with the clinical experience in residency.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".