Multimedia resources to support the recruitment of committed hematopoietic stem cell donors: Perspectives of the most‐needed donors
Bibliographic record
Abstract
BACKGROUND: Recruitment of committed unrelated hematopoietic stem cell donors from the most-needed demographics remains a challenge for donor recruitment organizations worldwide. Multimedia resources are gaining attention as a modality to support recruitment efforts; however, there is a lack of guidance for the development of such tools. This qualitative study explores the perspectives of eligible stem cell donors on an educational whiteboard video about stem cell donation, generating insights into how whiteboard videos and related multimedia may be optimized for donor recruitment. STUDY DESIGN AND METHODS: Eight semistructured focus groups were conducted with 38 potential donors from the most-needed demographics (young, male, and non-Caucasian) after they had watched a 3.5-minute whiteboard video explaining key concepts in stem cell donation (https://youtu.be/V4fVBtxnWfM). Constructivist grounded theory was used to identify themes and to develop a framework for understanding participants' preferred features of recruitment multimedia. RESULTS: Participants identified a range of features contributing to the effectiveness of recruitment multimedia, adding that the whiteboard video is an effective, integrated, and readily accessible format for supporting donor recruitment. Topics that participants felt are important to address include knowledge gaps regarding donation procedures, concerns about donor safety, and the particular need for specific donor demographics. Suggested avenues for improvement include the addition of donor/recipient/patient personal experiences, attention-grabbing hooks, and a call to action including opportunities for further learning. CONCLUSIONS: Several considerations were generated to inform the development of future multimedia for donor education/recruitment and are relevant to donor recruitment organizations worldwide.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".