Designing a Multidisciplinary Cardio-obstetrics Curriculum for General Cardiology and Obstetrics Residents: A National Survey of Educational Needs
Bibliographic record
Abstract
Background: The increasing and potentially preventable cardiac events in pregnant patients have led to calls to enhance multidisciplinary cardio-obstetrics education. To design a multidisciplinary cardio-obstetrics curriculum for general cardiology and obstetrics and gynecology (OBGYN) residents, we need to define educational needs from the perspectives of both cardiology and OBGYN residents. Our study characterizes the educational needs of Canadian cardiology and OBGYN residents. Methods: Canadian cardiology and OBGYN residents were surveyed on clinical exposures, perceived needs for topics, unperceived needs for topics (multiple-choice questions) and preferences for educational formats. High priorities were defined as ≥ 50% of responses indicating a perceived need or ≥ 50% indicating an unperceived need. Results: A total of 154 residents participated (cardiology n = 44, OBGYN n = 110). Residents reported insufficient clinical exposure to nearly all cardiac disorders, with 33% of exposures occurring in multidisciplinary contexts. All topics aside from gestational hypertension were rated as high priority on perceived needs by both specialties. High-priority unperceived needs were congenital heart disease (both specialties), pre-existing acquired heart disease (both specialties), medication safety (OBGYN), peripartum management (OBGYN), and pregnancy-related heart disease (OBGYN). Cardiology and OBGYN residents shared preferences for in-person simulation, virtual simulation, and online modules. Conclusions: Residents in both specialties reported low clinical exposure to most cardiac disorders during pregnancy, identified high-priority perceived needs in multiple topics, and shared 2 high-priority unperceived needs. OBGYN residents identified 3 additional high-priority unperceived needs. These data can inform design of multidisciplinary cardio-obstetrics curricula for general cardiology and OBGYN residents.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".