Transfusion camp: A retrospective study of self‐reported impact on postgraduate trainee transfusion practice
Bibliographic record
Abstract
BACKGROUND: The optimal method of postgraduate transfusion medicine (TM) education remains understudied. One novel approach is Transfusion Camp, a longitudinal 5-day program that delivers TM education to Canadian and international trainees. The purpose of this study was to determine the self-reported impact of Transfusion Camp on trainee clinical practice. STUDY DESIGN AND METHODS: A retrospective analysis of anonymous survey evaluations from Transfusion Camp trainees over three academic years (2018-2021) was conducted. Trainees were asked, "Have you applied any of your learning from Transfusion Camp into your clinical practice?". Through an iterative process, responses were categorized into topics according to program learning objectives. The primary outcome was the rate of self-reported impact of Transfusion Camp on clinical practice. Secondary outcomes were to determine impact based on specialty and postgraduate year (PGY). RESULTS: Survey response rate was 22%-32% over three academic years. Of 757 survey responses, 68% of respondents indicated that Transfusion Camp had an impact on their practice, increasing to 83% on day 5. The most frequent areas of impact included transfusion indications (45%) and transfusion risk management (27%). Impact increased as PGY increased with 75% of PGY-4+ trainees reporting impact. In multivariable analysis, the impact of specialty and PGY varied depending on the objective. DISCUSSION: The majority of trainees report applying learnings from Transfusion Camp to their clinical practice with variations based on PGY and specialty. These findings support Transfusion Camp as an effective means of TM education and help identify high-yield areas and gaps for future curriculum planning.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".