Building the foundation for a community-generated national research blueprint for inherited bleeding disorders: research priorities in health services; diversity, equity, and inclusion; and implementation science
Bibliographic record
Abstract
BACKGROUND: The National Hemophilia Foundation (NHF) conducted extensive all-stakeholder inherited bleeding disorder (BD) community consultations to inform a blueprint for future research. Sustaining and expanding the specialized and comprehensive Hemophilia Treatment Center care model, to better serve all people with inherited BDs (PWIBD), and increasing equitable access to optimal health emerged as top priorities. RESEARCH DESIGN AND METHODS: NHF, with the American Thrombosis and Hemostasis Network (ATHN), convened multidisciplinary expert working groups (WG) to distill priority research initiatives from consultation findings. WG5 was charged with prioritizing health services research (HSR); diversity, equity, and inclusion (DEI); and implementation science (IS) research initiatives to advance community-identified priorities. RESULTS: WG5 identified multiple priority research themes and initiatives essential to capitalizing on this potential. Formative studies using qualitative and mixed methods approaches should be conducted to characterize issues and meaningfully investigate interventions. Investment in HSR, DEI and IS education, training, and workforce development are vital. CONCLUSIONS: An enormous amount of work is required in the areas of HSR, DEI, and IS, which have received inadequate attention in inherited BDs. This research has great potential to evolve the experiences of PWIBD, deliver transformational community-based care, and advance health equity.
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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 | 0.273 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.026 | 0.031 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".