Bibliographic record
Abstract
BACKGROUND: Our university offers an interprofessional program to medical students in Year 1 of a 4-year undergraduate medical program: Health professional students learn from a health mentor-someone living with a chronic condition. This helps foster patient-centredness, empathy and communication skills. Long-term assessment of patient involvement in medical education is rare; thus, this study explores the lasting effects of 3-year post-program at entry-to-practice. METHODS: We conducted a case-based study of fourth-year medical students to evaluate the impact of learning from patients in the Health Mentors Program (HMP). Students analysed a video case of a person with cerebral palsy who fell at home and created a care plan. We compared students who participated in the HMP with those who did not, assessing how often they considered the patient's and caregiver's perspectives, the number of diagnostic tests ordered and referrals to other professionals and community services. FINDINGS: T-tests showed that HMP students significantly prioritised the patient's and caregiver's voices (p = 0.014, Cohen's d = 0.6) and ordered fewer diagnostic tests than non-HMP students (p = 0.001, Cohen's d = 3.3). However, there were no significant differences in medical consults, referrals to allied health professionals or community services. CONCLUSIONS: This was the first, limited attempt to use case-based assessments to measure the long-term impact of patient-centred learning. Integrating patient perspectives into preclinical education may enhance students' ability to work collaboratively with patients in care planning. Designing structured assessments around patient-centred care can help ensure that students retain and apply these skills in their clinical careers.
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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 | 0.006 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".