E‐learning in transfusion medicine: An exploratory qualitative assessment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: E-learning programmes are increasingly offered in transfusion medicine (TM) education. The aim of this study was to explore facilitators and barriers to TM e-learning programmes, including assessment of learning outcomes and measures of effectiveness. MATERIALS AND METHODS: Participants selected from a prior survey and representing a diverse number of international e-learning programmes were invited to participate. A mixed methodology was employed, combining a survey and individual semi-structured one-on-one interviews. Interview data were analysed inductively to explore programme development, evaluation, and facilitators and barriers to implementation. RESULTS: Fourteen participants representing 13 institutions participated in the survey and 10 were interviewed. The e-learning programmes have been in use for a variable duration between 5 and 16 years. Funding sources varied, including government and institutional support. Learner assessment methods varied and encompassed multiple-choice-questions (n = 12), direct observation (n = 4) and competency assessment (n = 4). Most regional and national blood collection agencies rely on user feedback and short-term learning assessments to evaluate their programmes. Only one respondent indicated an attempt to correlate e-learning with clinical practices. Factors that facilitated programme implementation included support from management and external audits to ensure compliance with regulatory educational and training requirements. Barriers to programme implementation included the allocation of staff time for in-house development, enforcing compliance, keeping educational content up-to-date and gaining access to outcome data for educational providers. CONCLUSION: There is evidence of considerable diversity in the evaluation of e-learning programmes. Further work is needed to understand the ultimate impact of TM e-learning on transfusion practices and patient outcomes.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".