A web-based simulation of a longitudinal clinic used in a 4-week ambulatory rotation: a cohort study
Bibliographic record
Abstract
BACKGROUND: Residency training takes place primarily on inpatient wards. In the absence of a resident continuity clinic, internal medicine residents rely on block rotations to learn about continuity of care. Alternate methods to introduce continuity of care are needed. METHODS: A web-based tool, Continuity of Care Online Simulations (COCOS), was designed for use in a one-month, postgraduate clinical rotation in endocrinology. It is an interactive tool that simulates the continuing care of any patient with a chronic endocrine disease. Twenty-three residents in internal medicine participated in a study to investigate the effects of using COCOS during a clinical rotation in endocrinology on pre-post knowledge test scores and self-assessment of confidence. RESULTS: Compared to residents who did the rotation alone, residents who used COCOS during the rotation had significantly higher improvements in test scores (% increase in pre-post test scores +21.6 [standard deviation, SD, 8.0] vs. +5.9 [SD 6.8]; p < .001). Test score improvements were most pronounced for less commonly seen conditions. There were no significant differences in changes in confidence. Residents rated COCOS very highly, recommending its use as a standard part of the rotation and throughout residency. CONCLUSION: A stand-alone web-based tool can be incorporated into an existing clinical rotation to help residents learn about continuity of care. It has the most potential to teach residents about topics that are less commonly seen during a clinical rotation. The adaptable, web-based format allows the creation of cases for most chronic medical conditions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".