Online simulations of ambulatory care for medical residents
Bibliographic record
Abstract
Context and setting Despite a shift in patient care from inpatient to outpatient settings, graduates of internal medicine training programmes in Canada have perceived weaknesses in ambulatory care training. Resident continuity clinics may better prepare graduates for ambulatory practice, but they are logistically difficult to organise. Alternative methods are needed to improve the learning of ambulatory care principles during residency. Why the idea was necessary Residents in the internal medicine residency programme at the University of Ottawa spend most time on inpatient wards, and outpatient rotations are short in duration. Without a continuity clinic, it is difficult for residents to learn the ambulatory care principles necessary for practice, specifically continuity of care and practice management. We found little in a formal literature search on how to improve learning of these topics in this setting. We developed web-based simulations of ambulatory care, which we called ‘Continuity of Care Online Simulations’ (COCOS), to help fill this void. What was done Using endocrinology as a model, we developed an ambulatory-care curriculum that emphasises: longitudinal care of disease (monitoring disease course or treatment, adjusting therapy, when and how to discontinue therapy); special situations (medical problems of pregnancy, perioperative care), and practice management (urgency of consultations, appropriate follow-up duration and tests, collaboration with other doctors). These objectives were used to create a template storyboard that depicts sequential appointments. By adding disease-specific content to this template, we created simulations of the longitudinal care of patients with those diseases. Following the template ensured the general objectives could be discussed in reference to each specific disease. We developed 3 cases for residents to use on a trial basis. These are accessible on the Internet and require no software other than an Internet browser. Throughout each case, residents are asked to make multiple clinical and practice management decisions based on real-life scenarios. Immediate feedback is shown in pop-up message screens. Users complete an online quiz before and after each case and results are electronically recorded. Evaluation of results and impact Informal feedback from a resident focus group was strongly positive. COCOS was easy to access and navigate, and each case required an average of 25 minutes to complete. Residents felt COCOS should be used in other ambulatory care rotations, and requested access to COCOS to help prepare for qualifying examinations. It stimulated self-directed learning and preceptors noted that residents were more proactive in discussing ambulatory care topics. We used endocrinology as a model, but the template storyboard allows cases to be easily written for any specialty. The program's ability to track resident's responses could potentially be used for individual as well as rotation evaluation. Software development and maintenance was costly, and it was time-consuming to create the template and the first case. However, subsequent cases were more easily created. Revisions, if needed, were easily made, allowing incorporation of the most recent evidence. COCOS may improve residents' learning of ambulatory care principles, particularly in the absence of a resident continuity clinic. It also has the potential to expose residents to uncommon conditions, assist with lifelong learning and be used by programmes to share teaching materials and content. We plan to add another 3 cases and conduct a final evaluation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".