Transfusion Camp: a prospective evaluation of a transfusion education program for multispecialty postgraduate trainees
Bibliographic record
Abstract
BACKGROUND: The optimal method of providing transfusion medicine (TM) education has not been determined. Transfusion Camp was established in 2012 at the University of Toronto as a centrally delivered TM education program for postgraduate trainees. The impact of Transfusion Camp on knowledge, attitudes, and self-reported behavior was evaluated. METHODS: Didactic lectures (delivered locally, by webinar, or recorded) and locally facilitated team-based learning seminars were delivered over 5 days during the academic year to 8 sites: 7 in Canada and 1 in the United Kingdom. Knowledge assessment using a validated 20-question multiple-choice exam was conducted before and after Transfusion Camp. Attitudes and self-reported behavior were collected through a survey. RESULTS: Over 2 academic years (July 2016 to June 2018), 390 trainees from 16 different specialties (predominantly anesthesia, 41%; hematology, 14%; and critical care, 7%) attended at least 1 day of Transfusion Camp. The mean pretest score was 10.3 of 20 (±2.9; n = 286) compared with posttest score of 13.0 (±2.8; n = 194; p < 0.0001). Lower pretest score and greater attendance (4-5 days compared with 1-3 days) were associated with larger improvement in posttest score; delivery format, specialty, and postgraduate year were not. Trainees reported an improvement in self-rated abilities to manage TM scenarios; 95% rated TM knowledge as very or extremely important in providing patient care; and 81% indicated that they had applied learning from Transfusion Camp into clinical practice. CONCLUSIONS: Transfusion Camp increased TM knowledge, fostered a positive attitude toward TM, and enabled a self-reported positive impact on transfusion practice in postgraduate trainees. It is a novel and scalable approach to delivering TM education.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".