Maintaining trust in a pandemic: Blood collection agency messaging to donors and the public during the early days of COVID-19
Bibliographic record
Abstract
COVID-19 has posed unprecedented challenges to health systems around the world, including bloodcollection agencies (BCAs). Many countries, such as Canada and Australia, that rely on non-remuneratedvoluntary donors, saw an initial drop in donors in the early days of the pandemic followed by a return tosufficient levels of the blood supply. BCA messaging plays a key role in communicating the needs of theblood operator, promoting and encouraging donation, educating, and connecting with the public anddonors. This paper reports on discourse analysis (Bloor and Bloor, 2013) of BCA messaging in Canadaand Australia from March 1-July 31, 2020 to understand how BCAs constructed donation to encouragedonation during this period and what this can tell us about public trust and blood operators. Drawing onmultiple sources of online content and print media, our analysis identified four dominant messagesduring the study period: 1) blood donation is safe; 2) blood donation is designated an essential activity;3) blood is needed; and 4) blood donation is a response to the pandemic. In Canada and Australia, ouranalysis suggests that: 1) implicit within constructions of blood donation as safe is the message thatBCAs can be trusted; 2) messages that construct blood donation as essential and needed implicitly askdonors to trust BCAs in order to share in the commitment of meeting patient needs; and 3) thepandemic has made possible the construction of blood donation as both an exceptional andcommonplace activity. For BCAs, our analysis supports donor communications that are transparent andresponsive to public concerns, and the local context, to support public trust. Beyond BCAs, healthorganizations and leaders cannot underestimate the importance of building and maintaining public trustas countries continue to struggle with containment of the virus and encourage vaccine uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".