A relational approach to understanding the factors influencing new plasma donor retention in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Understanding the experiences and perceptions of new plasma donors is important for developing strategies to retain them. MATERIALS AND METHODS: This qualitative study focuses on new donors' experiences with plasma donation, the factors that influence their interest in donating again and their thoughts about establishing a regular plasma donation routine. We conducted 48 one-on-one semi-structured interviews and used reflexive thematic analysis with a relational approach to donation to analyse these data. RESULTS: For new plasma donors in this study, interest in returning to donate again was facilitated by relational care, where donors were cared for by attentive staff, and felt they could care for others by donating. Their interest in helping others through ongoing donation was influenced by their relationships with people who have benefited from blood products or experienced illnesses they associated with plasma-derived medicines, as well as their sense of social responsibility and community belonging. The most prevalent deterrent to donating again was the experience of feeling unwell during or after donation. The practice of relational care from staff members can mitigate the fallout of the negative experience. Retention of new donors requires flexibility to ensure that donation is easy, convenient and does not negatively impact their health and ability to care for others in their social network. CONCLUSION: Investigating retention decisions for new plasma donors through the lens of relational care provides insight that can help blood collection agencies develop more effective strategies for retention in non-remunerated settings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".