Teaching multimorbidity to medical students
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is a rising health care phenomenon and doctors require specific skill sets to effectively care for patients with multiple illnesses. Despite this, most medical education is taught using a single-disease, systems-based approach. Consequently, students can struggle to manage patients with multimorbidity. To help final year medical students manage patients with multimorbidity in clinical practice, we devised, taught, and evaluated a heuristic: collect, cluster and co-ordinate. APPROACH: Students attended a 1-hour online workshop during their family medicine clerkship. Using a flipped classroom design, students watched a podcast, followed by facilitated small-group work. EVALUATION: Out of 132 final-year medical students, 102 participated in the evaluation. Students rated their confidence managing patients with multimorbidity, pre and post teaching on a Likert scale. Prior to teaching, 36% (n = 37) students rated their ability to manage a patient with multimorbidity as slightly confident. After teaching, 74.5% (76) students rated their ability to manage the same patient as fairly or completely confident. Prior to graduation students were surveyed to determine if they had applied the framework during clinical placements. Sixty-one students responded; 32 applied the heuristic during family medicine and in other clinical rotations such as paediatrics, obstetrics, emergency medicine and anaesthesia. IMPLICATIONS: Specific instruction on managing consultations with patients experiencing multimorbidity during undergraduate medical education increased learner confidence caring for these patients. The heuristic was relevant and applied in disciplines outside family medicine. Students indicated that earlier teaching on this topic would have prepared them better for clinical placements.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".