Development and evaluation of checklists to support the recruitment of committed hematopoietic stem cell donors
Bibliographic record
Abstract
BACKGROUND: Checklists are memory recall tools used across healthcare to improve outcomes. Here, we describe the development and evaluation of checklists to support recruitment of committed allogeneic hematopoietic stem cell donors. STUDY DESIGN AND METHODS: Checklists were developed with the following objectives: (1) improve best-practice adherence; (2) reduce errors; and (3) support standardization at stem cell drives. Topics included: recruiting needed donors; securing informed consent; maintaining good-documentation practices; and supervising registration and tissue sample collection. Checklists were iteratively revised with input from stakeholders. We evaluated the checklists by examining recruitment outcomes and errors (i.e., preventing registrants from being listed as donors) pre- (11/2011-8/2016) and post- (9/2016-11/2019) implementation by the Canadian donor recruitment organization Stem Cell Club. Quantitative and qualitative methods were employed to analyze recruiters' perspectives on the checklists. RESULTS: The checklists supported recruitment of donors from needed demographic groups as Stem Cell Club expanded its recruitment effort from 4118 registrants (60% male, 58% non-European) pre-implementation to 10,621 (52% male, 56% non-European) post-implementation. Checklist implementation was associated with a marked reduction in errors (from 13.2% to 1.9%) and a three-fold increase in the match rate of recruited donors (from 0.024% to 0.075%). Qualitative and quantitative analysis of recruiter feedback supported that the checklists' objectives were realized from the recruiter perspective. DISCUSSION: We developed checklists to support donor recruitment and showed that their implementation was valued by recruiters and associated with both reduced errors and improved donor recruitment outcomes. The checklists 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 | 0.302 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".