Transitions curriculum impact on students and care
Bibliographic record
Abstract
BACKGROUND: Transitions of patient care from the inpatient to outpatient setting is a high-risk time often resulting in medical errors and adverse events. Transitions of care programmes have been demonstrated to reduce negative outcomes. Several professional societies have highlighted care transitions as a central pillar of patient care and therefore a crucial aspect of health professional education; however, little data exist on medical student education in this area. APPROACH: The Transitions of Care Curriculum was developed and delivered to all Harvard Medical School Core I Internal Medicine Clerkship students at Beth Israel Deaconess Medical Center, Boston, MA between January 2017 and March 2019, where 12-14 students participated each quarter and included didactic teaching followed by experiential learning. Student data were collected via postclerkship survey. Patient data were collected via chart review. Student self-reported comfort level with transitions in care skills and medical errors were analysed. EVALUATION: All student measures related to comfort with transitions in care skills demonstrated statistically significant improvement after curriculum participation(p < 0.001). Of the patients with a completed postdischarge note, students identified ≥1 postdischarge related issue in 33 of 70 patients, with multiple issues identified in many of these patients. Seventy-six total issues were identified. IMPLICATIONS: The Transitions of Care Curriculum demonstrated promising student and patient outcomes, suggesting that students can successfully learn and advance clinical skills while having a positive impact on a highly needed and important aspect of patient care by reducing postdischarge errors and adverse events.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".