College complaints against resident physicians in Canada: a retrospective analysis of Canadian Medical Protective Association data from 2013 to 2017
Bibliographic record
Abstract
<h3>Background:</h3> An understanding of regulatory complaints against resident physicians is important for practice improvement. We describe regulatory college complaints against resident physicians using data from the Canadian Medical Protective Association (CMPA). <h3>Methods:</h3> We conducted a retrospective analysis of college complaint cases involving resident doctors closed by the CMPA, a mutual medicolegal defence organization for more than 100 000 physicians, representing an estimated 95% of Canadian physicians. Eligible cases were those closed between 2008 and 2017 (for time trends) or between 2013 and 2017 (for descriptive analyses). To explore the characteristics of college cases, we extracted the reason for complaint, the case outcome, whether the complaint involved a procedure, and whether the complaint stemmed from a single episode or multiple episodes of care. We also conducted a 10-year trend analysis of cases closed from 2008 to 2017, comparing cases involving resident doctors with cases involving only nonresident physicians. <h3>Results:</h3> Our analysis included 142 cases that involved 145 patients. Over the 10-year period, college complaints involving residents increased significantly (<i>p</i> = 0.003) from 5.4 per 1000 residents in 2008 to 7.9 per 1000 in 2017. While college complaints increased for both resident and nonresident physicians over the study period, the increase in complaints involving residents was significantly lower than the increase across all nonresident CMPA members (<i>p</i> < 0.001). For cases from the descriptive analysis (2013–2017), the top complaint was deficient patient assessment (69/142, 48.6%). Some patients (22/145, 15.2%) experienced severe outcomes. Most cases (135/142, 97.9%) did not result in severe physician sanctions. Our classification of complaints found 106 of 163 (65.0%) involved clinical problems, 95 of 163 (58.3%) relationship problems (e.g., communication) and 67 of 163 (41.1%) professionalism problems. In college decisions, 36 of 163 (22.1%) had a classification of clinical problem, 66 of 163 (40.5%) a patient–physician relationship problem and 63 of 163 (38.7%) a professionalism problem. In 63 of 163 (38.7%) college decisions, the college had no criticism. <h3>Interpretation:</h3> Problems with communication and professionalism feature prominently in resident college complaints, and we note the potential for mismatch between patient and health care provider perceptions of care. These results may direct medical education to areas of potential practice improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".