In-Person or Online? The Effect of Delivery Mode on Team-Based Learning of Clinical Reasoning in a Family Medicine Clerkship
Bibliographic record
Abstract
In health professions education, team-based learning (TBL) has been used to help learners develop clinical reasoning and decision-making skills. The COVID-19 pandemic has challenged institutions to move curriculum delivery from largely in-person to online. With the anticipated return to in-person instruction and arguments made in favor of online instruction in certain circumstances, evidence is needed to support decision making in curriculum planning. The purpose of this study was to examine the effect of delivery mode (in-person vs. online) on student learning of clinical reasoning and clinical decision-making (CR/CDM) in the family medicine clerkship. Data from three cohorts of third-year medical students were included in the study: 2018/2019 cohort, in-person; 2019/2020 cohort, half of the cohort in-person, half of the cohort online; 2020/2021 cohort, online. Students' performance data-individual readiness assurance test (IRAT) and group readiness assurance test (GRAT) scores-were used. The Generalized Estimating Equations (GEE) analysis was performed. As expected, students scored higher in GRAT than IRAT across the three cohorts. No significant IRAT-GRAT differences were observed between in-person and online delivery of TBL sessions. Student learning of CR/CDM in TBL is comparable between the two modes of delivery in the family medicine clerkship. Future research in other clerkships, years of medical education, and professional programs is needed to inform decision making regarding the TBL delivery mode.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".