Tuition Costs of Master’s of Health Professions Education Programs: A Cross-Sectional Analysis
Bibliographic record
Abstract
PURPOSE: The increasing professionalization of medical education during the past 2 decades has ushered in an era in which formal degrees, particularly master's of health professions education (MHPE), have become important for career advancement in medical education. Although tuition costs can pose a substantial barrier for many seeking advanced degrees in health professions education, data on tuition associated with these programs are lacking. This study examines the accessibility of pertinent cost-related information available to prospective students and the variability of costs among programs worldwide. METHOD: The authors conducted an Internet-based, cross-sectional study, augmented with emails and direct contact with educators, to extract tuition-related data for MHPE programs between March 29, 2022, and September 20, 2022. Costs were converted to an annual total within each jurisdiction's currency and converted to U.S. dollars on August 18, 2022. RESULTS: Of the 121 programs included in the final cost analysis, only 56 had publicly available cost information. Excluding programs free to local students, the mean (SD) total tuition cost was $19,169 ($16,649), and the median (interquartile range) cost was $13,784 ($9,401- $22,650) (n = 109). North America had the highest mean (SD) tuition for local students ($26,751 [$22,538]), followed by Australia and New Zealand ($19,778 [$10,514]) and Europe ($14,872 [$7,731]), whereas Africa had the lowest ($2,598 [$1,650]). The region with the highest mean (SD) tuition for international students was North America ($38,217 [$19,500]), followed by Australia and New Zealand ($36,891 [$10,397]) and Europe ($22,677 [$10,010]), whereas Africa had the lowest ($3,237 [$1,189]). CONCLUSIONS: There is substantial variability in the geographic distribution of MHPE programs and marked differences in tuition. Incomplete program websites and limited responsiveness from many programs contributed to a lack of transparency regarding potential financial implications. Greater efforts are necessary to ensure equitable access to health professions education.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".