P.106 A pilot-project for neurosurgery competency-based design implementation
Bibliographic record
Abstract
Background: In preparation for July 2019 rollout of competency-based design (CBD) in Canadian neurosurgery residency training, the University of Calgary launched a pilot-program of five representative EPAs using the One45 program. Our study objectives were to examine the uptake of CBD with residents and faculty and to quantify CBD implementation barriers. Methods: Phase one of the One45-based CBD pilot-program launched on November 1st, 2018 and ended on January 8th, 2019, after which a questionnaire was sent to each participating resident. The questionnaire examined number of EPAs initiated, measures of favourability, importance, ease of use, and barriers encountered. Results: Results obtained from the survey show 93.8% response rate (15/16 residents). 66.7% of residents feel that CBD is moderately important or higher to their education. Over the 10 study weeks, there were only 8 completed EPAs (expected was 50), five of which were completed by a single resident. Major expressed barriers of implementation of CBD were time involved (50.0%) and technical unfamiliarity with the platform itself (50.0%). Conclusions: This study demonstrates the critical importance of piloting a CBD program prior to official implementation as immediate buy-in was significantly slower than anticipated. Technical and time barriers exist which need to be rectified in advance of July 2019.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".