Remediation of Residents in Difficulty
Bibliographic record
Abstract
PURPOSE: To determine, through a 10-year review, (1) the prevalence of residents in difficulty, (2) characteristics of these residents, (3) areas of residents' weakness, and (4) outcomes of residents who undergo remediation. METHOD: A retrospective review of resident records for the University of Toronto Faculty of Medicine's (UT-FOM) Board of Examiners for Postgraduate Programs (BOE-PG) was done from July 1, 1999 to June 30, 2009 using predetermined data elements entered into a standardized form and analyzed for trends and significance. Outcomes for residents in difficulty were tracked through university registration systems and licensure databases. RESULTS: During 10 years, 103 UT-FOM residents were referred to the BOE-PG, representing 3% of all residents enrolled. The annual prevalence of residents referred to the BOE-PG ranged from 0.2% to 1.5%. The CanMEDS framework was used to classify areas of residents' weaknesses and organize remediation plans. All 100 residents studied had either medical expertise (85%) or professionalism (15%) weaknesses or both. Residents had difficulties with an average of 2.6 CanMEDS Roles, with highest frequencies of Medical Expert (85%) Professional (51%), Communicator (49%), Manager (43%), and Collaborator (20%). Often, there were multiple remediation periods, with an average of six months' duration. Usually, remediation was successful; 78% completed residency education, 17% were unsuccessful, and 5% remained in training. CONCLUSION: Residents in difficulty have multiple areas of weakness. The CanMEDS framework is an effective approach to classifying problems and designing remediation plans. Successful completion of residency education after remediation is the most common outcome.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".