Research partnerships between blood services and public health authorities: An international, cross‐sectional survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The COVID-19 pandemic has brought to the fore how blood services can partner with public health (PH) authorities to inform decisions. Yet the scope of partnerships between blood services and PH authorities is inadequately documented. We explored how blood services partner with PH authorities outside the scope of COVID-19. MATERIALS AND METHODS: On 19 January 2022, survey was sent to employees of blood services located throughout the world. Survey questions mainly pertained to partnerships with PH authorities, including how blood specimens are used and collected. RESULTS: Twenty-seven recipients-4 (14.8%) in Africa, 3 (11.1%) in Asia, 9 (33.3%) in Europe, 6 (22.2%) in North America, 2 (7.4%) in Oceania and 3 (11.1%) in South America-completed the survey. Fifteen recipients (55.6%) indicated their blood service was directly or indirectly supervised by PH authorities. Twenty-four recipients (88.9%) indicated currently using or planning to use blood donor data or samples for PH research or pathogen surveillance. A substantial proportion of respondents reported using samples or results from non-routine tests for the surveillance of non-transfusion-transmitted infectious disease pathogens (n = 13 [48.1%]); samples or results of non-routine tests for PH research unrelated to pathogens (n = 10 [37.0%]); donor data for PH research unrelated to pathogens (n = 12 [44.4%]) and donor data for PH research unrelated to transfusion safety (n = 11 [40.7%]). Fourteen (51.9%) had established (or planned to establish) longitudinal cohorts and 19 (70.4%) biobanks. CONCLUSION: The majority of responding blood services were already involved in or planned to be involved in PH research or pathogen surveillance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".