Resident evaluations in the age of competency-based medical education: faculty perspectives on minimizing burdens
Bibliographic record
Abstract
OBJECTIVE: Competency-based medical education (CBME), an outcomes-based approach to medical education, continues to be implemented across many postgraduate medical education programs worldwide, including a recent introduction into Canadian neurosurgical training programs (July 2019). The success of this educational paradigm shift requires frequent faculty observation and evaluation of residents performing defined tasks of the specialty. A main challenge involves providing residents with frequent performance evaluations and feedback that are feasible for faculty to complete. This study aims to define what is currently happening and what changes are needed to make CBME successful for the certification of neurosurgeons' competence. METHODS: A 55-item questionnaire was emailed nationwide to survey Canadian neurosurgical faculty. RESULTS: Fifty-two complete responses were received and achieved a distribution highly correlated with the number of faculty neurosurgeons practicing in each Canadian province (Pearson's r = 0.94). Two-thirds (35/52) of faculty reported currently taking a median of 10 minutes to complete evaluation forms at the end of a resident's rotation block. Regardless of the faculty's province of practice (p = 0.50) or years of experience (p = 0.06), they reported 3 minutes (minimum 1 minute, maximum 10 minutes, interquartile range [IQR] 3 minutes) as a feasible amount of time to spend completing an evaluation form following an observation of a resident's performance of an entrustable professional activity (EPA). If evaluation forms took 3 minutes to complete, 85% of respondents (44/52) would complete EPA evaluations weekly or daily. The faculty recommended 5 minutes as a feasible amount of time to provide oral feedback (minimum 1 minute, maximum 20 minutes, IQR 3.25 minutes), which was significantly higher (p = 0.00099) than their recommended amount of time for completing evaluation forms. The majority of faculty (71%) stated they would prefer to access resident evaluation forms through a mobile application compared to a paper form (12%), an evaluation website (8%), or through a URL link sent via email (10%; p = 0.0032). CONCLUSIONS: To facilitate the successful implementation of CBME into a neurosurgical training curriculum, resident EPA assessment forms should take 3 minutes or less to complete and be accessible through a mobile application.
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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 | 0.033 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".