A Survey to Assess the Educational Needs of Clinicians in Maternal Hematology
Bibliographic record
Abstract
Introduction With advancement in medical knowledge and comprehensive care more women with hematological disorders are proceeding with pregnancy. These women have unique needs during the perinatal period, where the intersection of the Hematological disease and pregnancy can mutually impact each other, resulting in significant morbidity and even mortality. Hematologists, Internal Medicine (IM) physicians and Obstetricians are increasingly faced with the complex care of these women. The aim of this study is to assess the learning needs of these stakeholders in the care of pregnant women with hematological conditions to direct the development of educational activities. Methods We conducted a cross-sectional study using a self-administered electronic questionnaire sent to Hematologists, Obstetricians, Maternal-Fetal Medicine (MFM) Specialists and IM Specialists across Canada. The study was approved by the Research Ethics Board of Sinai Health System in Toronto, Ontario. The questionnaire assessed physician characteristics, physicians' previous educational experiences in Maternal Hematology, self-assessment of knowledge and confidence in managing pregnancy in these women, and preferences for further learning activities in Maternal Hematology. The questionnaire was pilot tested by two hematology resident physicians and two MFM fellow physicians at the University of Toronto and modifications were made to the survey based on this feedback. The survey was administered electronically in English in collaboration with the Canadian Hematology Society and Canadian MFM program directors, using an online survey-distributing website, Survey Monkey™. Participation was voluntary and anonymous. Data were exported into Excel (Microsoft, Redmond, Washington, USA) for statistical analysis. Following overall descriptive analyses (frequencies and proportions), responses were further tabulated by participant specialty. Results The survey was administered from December 2019 to March 2020. 97 participants responded to the survey, and 82 (84.5%) completed it. The 15 (15.5%) incomplete surveys (completion of less than 25% of the questionnaire), were excluded from analysis. Respondents included 17 (20.7 %) MFM specialists, 21 (25.6%) Obstetricians, 34 (41.5%) hematologists and five (6.1 %) IM physicians. Half of respondents had < 5 years of practice (52.6%%) and there was a similar distribution of respondents with 5-10 years (12.8%) and 11-15 years (12.8%) in practice. Most respondents were female (69.6%). Overall, 38% of respondents described the quality of their prior educational experiences as 'very useful' or 'extremely useful'. Approximately half of the respondents rated their knowledge of Maternal Hematology as intermediate, 25% as beginner and 25% as advanced. The majority of Hematologists and IM specialists (64%) considered their knowledge in Maternal Hematology as 'intermediate'; specialists in Obstetrics and MFM were divided with 30.8% considering their knowledge at the 'beginner' level and 38.5% at the 'intermediate' level. Only 3.7% of respondents described their knowledge level as 'expert'. Most respondents in Obstetrics and MFM (84.2%) considered knowledge of how to diagnose venous thromboembolism during pregnancy as 'extremely important followed by knowledge of thromboprophylaxis (63.2%) and therapeutic anticoagulation during pregnancy (63.2%) (Figure 1). Amongst Hematology and IM physicians, thrombotic microangiopathy was rated by the greatest proportion of respondents (59%) as being "extremely important". For the format of educational activity, three quarters of the respondents preferred a blended learning program, which would include a combination of face-to-face teaching and technology-enhanced learning activities. Respondents predominantly (76.2%) agreed that learning collaboratively with other specialties in a combined Maternal Hematology program would be helpful. Conclusion These results suggest that an interdisciplinary learning program involving collaboration across relevant subspecialties would be preferred by participants for the teaching and learning of Maternal Hematology. A program involving blended (in-person and technology-enhanced) learning modalities was viewed favorably by participants, and should aim to incorporate various topics, which may be prioritized differently amongst specific subspecialties. Disclosures Malinowski: Alexion: Other: Advisory board; Alexion: Honoraria. Shehata:Ferring: Honoraria.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".