Development and Evaluation of a Whiteboard Video Series to Support the Education and Recruitment of Committed Unrelated Donors for Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Whiteboard videos are a popular video format, allowing viewers to see drawings of concepts alongside explanatory text and speech. We hypothesized that whiteboard videos could support the education and recruitment of unrelated stem cell donors in Canada. A series of 5 sharable whiteboard videos about stem cell donation was produced and posted online in September 2018, including 1 full-length video (https://youtu.be/V4fVBtxnWfM) and 4 shorter videos titled "What Is Stem Cell Transplantation?" "How Does the Matching Process Work?" "How Are Stem Cells Donated?" and "How Can I Register as a Stem Cell Donor?" In the videos, metaphorical interpretations of stem cells as factories and genetic markers as barcode labels are employed to communicate complex concepts. The particular need for young, male, and ethnically diverse donors is reflected in the characters portrayed. Surveys demonstrated the videos (1) were used and valued by stakeholders in donor recruitment and (2) significantly improved objective and self-reported knowledge about stem cell donation and reduced donation-related ambivalence among viewers from the most-needed donor demographics. Use of the whiteboard videos was also associated with improved donor recruitment outcomes in Canada. Our work is relevant to donor registries and recruitment organizations worldwide that seek to improve their recruitment efforts.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".