Targeted online education in obstetric hematology significantly improves resident knowledge and addresses a critical training gap
Bibliographic record
Abstract
Abstract Introduction The care of pregnant individuals with hematologic disorders requires the expertise of two distinct medical specialties to manage a highly vulnerable patient population. Given the paucity of evidence-based guidance in obstetric hematology, education in this field often relies on select individuals with clinical experience. This poses challenges for smaller teaching institutions, where such expertise may not be readily available. Since the COVID-19 pandemic, it is now established that online medical education is effective, flexible, and generally well accepted by residents. However, success hinges on purposeful design, interactivity, and institutional commitment. This study aimed to 1) assess the learning needs of obstetric and hematology residents and 2) evaluate the efficacy of an educational delivery model to address these needs. Methods We conducted a cross-sectional educational needs assessment of obstetrics and hematology trainees in Switzerland using a self-administered electronic questionnaire distributed by 12 obstetrics residency program directors and 14 hematology residency program directors. The survey assessed perceived educational needs, motivation, preferred topics, and learning styles. The survey was administered electronically in English using the online survey-distributing website Jotforms®. Participation was voluntary and anonymous. We subsequently developed three online educational modules addressing the management of pregnant patients with bleeding disorders, venous thromboembolism (VTE) and sickle cell disease (SCD). Each online module included a pre- and post-module assessment with 10 case-based questions. Modules were offered as optional preparation for a national hematology resident workshop available on a public homepage www.obstetric-hematology.com. The modules were designed with RiseArticulate® and pre- and post-module scores were assessed through Grassblade LRS®, a Learning Record Store that collects and stores learning data from e-learning tools using the xAPI (Tin Can API) standard anonymously. Data were exported into Excel® for statistical analysis. A paired t-test was used to compare pre- and post- test scores. Learner satisfaction and perceived clinical relevance were assessed using a five-question Likert-scale. Results A total of 35 hematology residents, 27 obstetrics residents, and 28 obstetric early-career attendings completed the needs assessment questionnaire. Of 91 respondents, 84% (n=76) expressed interest in further training in obstetric hematology, most commonly motivated by clinical exposure during their practice (n= 80, 88%). Online modules were the preferred learning format, receiving an average rating of 4.1/ 5. The top three topics of highest interest included bleeding disorders, antiphospholipid syndrome (APS), and thrombosis in pregnancy. We developed three online modules on bleeding disorders and pregnancy, venous thromboembolism (VTE) in pregnancy and sickle cell disease (SCD) and pregnancy. SCD instead of APS was added to address a high-risk population with unique challenges that is often underrepresented in standard training. Bleeding disorders in pregnancy was the most accessed module (n=36), followed by VTE and pregnancy (n=34) and SCD and pregnancy (n=14). Completion rates for pre and post-module tests were: bleeding disorders 31% (n = 11), VTE 38% (n = 13), SCD 50% (n = 7). Ten modules were completed with the pretest only and were excluded from the data analysis. Mean test scores improved significantly following completion of the modules from 48% to 72% (p < 0.01) for bleeding disorders, 65% to 89% (p < 0.01) for VTE, and 56% to 76% (p = 0.03) for SCD Five learners evaluated the content. They found the platform supportive (4.6/5), rated the content as clear (4.8/5), helpful in building confidence for management (4.4/5) and in improving patient care (4.4/5). Conclusions These results demonstrate an unmet need for accessible and widely available training in obstetric hematology. Our online modules produced an immediate improvement in knowledge across the three key topics in obstetric hematology. Whether this knowledge impacts participants' clinical practice remains to be evaluated.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".