Targeted online education in obstetric hematology significantly improves resident knowledge and addresses a critical training gap
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».