Donor Research in Australia: Challenges and Promise
Bibliographic record
Abstract
Donors are the key to the core business of Blood Collection Agencies (BCAs). However, historically, they have not been a focus of research undertaken by these organizations. This model is now changing, with significant donor research groups established in a number of countries, including Australia. Donor research in the Australian Red Cross Blood Service (Blood Service) is concentrated in the Donor and Community Research (DCR) team. Cognizant of the complex and ever-changing landscape with regard to optimal donor management, the DCR team collaborates with academics located at universities around Australia to coordinate a broad program of research that addresses both short- and-long term challenges to the blood supply. This type of collaboration is not, however, without challenges. Two major collaborative programs of the Blood Service's research, focusing on i) the recruitment and retention of plasmapheresis donors and ii) the role of the emotion pride in donor motivation and return, are showcased to elucidate how the challenges of conducting collaborative BCA research can be met. In so doing, these and the other research programs described herein demonstrate how the Blood Service supports and contributes to research that not only revises operational procedures but also contributes to advances in basic science.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| 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".