Transfusion Camp Rwanda 2023: A train‐the‐trainer workshop establishing locally driven leadership in knowledge translation and sustainability in transfusion medicine education
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Blood transfusion is performed daily in hospitals. Gaps exist between transfusion guidelines and day-to-day clinical care. These gaps are prevalent in resource-limited settings due to scarce continuing medical education. Transfusion Camp Rwanda aims to bridge this gap by (1) delivering context-appropriate up-to-date education, (2) teaching participants how to independently deliver a case-based curriculum and (3) identifying strategies to promote change in transfusion practice in Rwanda. MATERIALS AND METHODS: In May 2023, a multidisciplinary team from Canada and Rwanda carried out a Transfusion Camp train-the-trainer workshop for clinicians from all five provinces in Rwanda. Participants attended in-person lectures, seminars and workshop group discussions on the implementation of the Rwanda National Directives on Rational Use of Blood and Blood Components. Course feedback was based on the Kirkpatrick Model of Training and Evaluation. RESULTS: Fifty-one physicians and laboratory technicians participated in the course. Confidence in caring for patients based on transfusion guidelines was self-rated as 'excellent' by 23% of participants before and 77% after, while 84% reported they planned to teach Transfusion Camp to others and 100% responded that they will apply course content to clinical practice. Workshop groups recommended strategies to improve transfusion medicine practice in Rwanda in four domains: Communication, Institutional Approval, Practice Audits and Education. CONCLUSION: Transfusion medicine education in Rwanda using a train-the-trainer approach was well-received by participants and allowed for a more detailed understanding of the local medical and educational environment. These observations can inform the further expansion of the Transfusion Camp Rwanda project.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".