Assessing Student Attitudes Regarding Cost-Consciousness in Medical Education
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Purpose: The purpose of this study was to compare attitudes regarding cost-consciousness between student populations at two medical schools in the United States and Canada. Method: We conducted a cross-sectional survey of students at Harvard Medical School and University of Toronto. We performed chi-square analyses comparing responses from the two institutions. Results: Response rates were 48% (n=162) and 45% (n=228) at Harvard and the University of Toronto, respectively. At both institutions, >96% of students agreed clinicians at all stages of training should be familiar with cost-conscious decision-making, 80% agreed physicians are responsible for discussing healthcare costs with patients, and over 80% felt they had too little education on the topic in medical school. Students differed in opinions about the extent to which patients should inquire about costs, with students at Harvard more likely to endorse this opinion compared with those from Toronto (51% vs 28%, respectively), and differed over whether cost-consciousness led to rationing of healthcare (Harvard 30% vs Toronto 51%). Fewer than 10% of all students expressed concerns that incorporating costs into care was unethical. Overall, 85% of students from both countries would like more formal teaching on this topic. Discussion: Students from both schools strongly endorsed a need to learn more about cost-conscious decision-making. Findings suggest students in both systems can benefit from learning similar core concepts related to high-value, cost-conscious care, and teaching in this topic can be customized to reflect specific differences in expectations and practices in the two healthcare systems.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".