Development and implementation of a mobile version of the O-SCORE assessment tool and case log for competency-based assessment in urology residency training: An initial assessment of utilization and acceptance among residents and faculty
Bibliographic record
Abstract
INTRODUCTION: In competency-based models of residency training, work-based assessments of residents' technical skills are essential both for providing formative feedback and for assessing surgical competence. The Ottawa Surgical Competency Operating Room Evaluation (O-SCORE) is a previously validated paper-based evaluation tool created to assess a surgical trainees' operative competence. To address some of the barriers to assessment, we developed and implemented a mobile application that combines the O-SCORE with a surgical case log. METHODS: A description of the development implementation process for the mobile O-SCORE and case log is provided. Following implementation, a survey was developed and administered electronically to all faculty and residents within the University of Ottawa's Division of Urology to assess user perceptions and utilization of the application. The survey was administered and data collected via Survey Monkey. RESULTS: The overall response rate was 94%. The majority of residents (94%) reported that it was easy to log cases with the application and 81% felt that it had a positive impact on their training; 75% of faculty were willing or very willing to complete evaluations when assigned and 66% felt that the application had a positive effect on the quality of feedback they provided. CONCLUSIONS: Overall, faculty and residents felt that our mobile O-SCORE application was user-friendly and valuable as both a surgical log and assessment tool. With surgical programs moving towards competency-based models of training and assessment, the O-SCORE mobile application represents a practical electronic surgical log and work-based assessment instrument that can be easily adopted into any surgical training program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".