Difficulties in Residency: An Examination of Clinical Rotations and Competencies Where Family Medicine Residents Most Often Struggle
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Remediation in residency is expensive; however, most research has focused on general approaches to remediation, with minimal investigation into whether there are patterns to the competencies or rotations that are most difficult for residents. Acquiring this information may improve future physician training and potentially reduce the frequency of resource-intensive remediation. We aimed to determine the competencies and rotations most challenging for family medicine residents, as defined by the number of assessments with flags (one or more competencies indicated as less than satisfactory). METHODS: A secondary data analysis of archived resident files from a large Canadian family medicine residency program was conducted. Residents from six cohorts were reviewed (N=393) and flags on the in-training evaluation reports (ITERs) and summative periodic progress reports were recorded and summarized with descriptive statistics. RESULTS: One hundred forty-one residents (36%) received at least one flag during training. Rotations where learners received the most flags were: internal medicine (average 1.52±4.82 flags), urban family medicine (average 1.48±4.18), and obstetrics (average 1.07±3.80). For residents having at least one flag, competencies causing most difficulty included: professionalism (21.4%), clinical decision making (17.8%), and teamwork and communication (15.5%). CONCLUSIONS: The file review identified coronary care unit, internal medicine, obstetrics, and general surgery as those rotations (adjusted for length) where family medicine residents most often struggled. Furthermore, deficient clinical knowledge was not one of the main reasons that residents are flagged. These findings may inform programs about where to target resident supports and resources.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".