Avoid reinventing the wheel: implementation of the Ottawa Clinic Assessment Tool (OCAT) in Internal Medicine
Bibliographic record
Abstract
Abstract Background Workplace based assessment (WBA) is crucial to competency-based education. The majority of healthcare is delivered in the ambulatory setting making the ability to run an entire clinic a crucial core competency for Internal Medicine (IM) trainees. Current WBA tools used in IM do not allow a thorough assessment of this skill. Further, most tools are not aligned with the way clinical assessors conceptualize performances. To address this, many tools aligned with entrustment decisions have recently been published. The Ottawa Clinic Assessment Tool (OCAT) is an entrustment-aligned tool that allows for such an assessment but was developed in the surgical setting and it is not known if it can perform well in an entirely different context. The aim of this study was to implement the OCAT in an IM program and collect psychometric data in this different setting. Using one tool across multiple contexts may reduce the need for tool development and ensure that tools used have proper psychometric data to support them. Methods Psychometrics characteristics were determined. Descriptive statistics and effect sizes were calculated. Scores were compared between levels of training (juniors (PGY1), seniors (PGY2s and PGY3s) & fellows (PGY4s and PGY5s)) using a one-way ANOVA. Safety for independent practice was analyzed with a dichotomous score. Variance components were generated and used to estimate the reliability of the OCAT. Results Three hundred ninety OCATs were completed over 52 weeks by 86 physicians assessing 44 residents. The range of ratings varied from 2 (I had to talk them through) to 5 (I did not need to be there) for most items. Mean scores differed significantly by training level (p
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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 | 0.046 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".