E‐learning/online education in transfusion medicine: A cross‐sectional international survey
Bibliographic record
Abstract
OBJECTIVES: This survey aims to assess the scope of transfusion e-learning courses in blood establishments and transfusion services internationally. BACKGROUND: E-learning/online education is increasingly used in the education of medical professionals. There is limited published data on the use of e-learning for transfusion medicine. MATERIAL AND METHODS: An International survey was designed and distributed to all members of the International Society of Blood Transfusion to assess utilisation of e-learning in their institutions. Descriptive statistics were used to summarise the results. RESULTS: A total of 177 respondents participated, 68 of which had e-learning modules in their institutions. Approximately two-thirds of the courses were developed in-house (66%), and 63% are available to learners from outside the host institutions. In one-third of institutions, these courses were established during the COVID-19 pandemic, while 15% had used e-learning courses for more than 10 years. The courses target different audiences and topics ranging from blood donation to hemovigilance. The most common audiences were physicians (71%), laboratory scientists/technologists (69%) and transfusion practitioners (63%). Formal assessment of learning outcomes is used in 70% of the programs. CONCLUSIONS: The survey demonstrates the widespread use of e-learning courses in transfusion education, with a substantial proportion being developed during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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 teacher head, 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".