How do people become plasma and platelet donors in a VNR context?
Bibliographic record
Abstract
BACKGROUND: The demand for therapeutic plasma-derived products poses a challenge to blood collection agencies (BCAs). In 2014-2015, the volume of plasma sent for fractionation met 17.7% of Quebec's needs for immunoglobulins. This article aims to offer an exploration of the paths blood donors follow in order to become plasma and platelet donors (PPDs). STUDY DESIGN AND METHOD: This analysis is based on semi-structured interviews with 50 PPDs in Quebec, Canada. Our analysis focused on the occurrence of events and the presence of contextual elements identified through: (1) factual data on PPDs; and (2) what PPDs identified as being an influence on their donation experience. This information was synthesized using a typology of trajectories. RESULTS: Six typical trajectories have been distinguished, first by the presence (19/50 respondents) or absence (31/50) of blood donation as a family tradition. Of the latter 31 donors, some pointed instead to inherited family values as having a significant influence on their commitment (11/31). Donors' careers were then distinguished as having started early (34) or late (16). Sub-types then appeared with the addition of other contextual elements, motivation profiles, and circumstances under which the conversion to apheresis donation occurred. CONCLUSION: Our findings suggest the existence of diversified donor trajectories, and confirm the importance of conducting more in-depth analyses of the sequence of events occurring along PPDs career. BCAs should develop strategies carefully tailored to different potential clienteles if they wish to convert whole blood donors to apheresis donation, and also focus on recruiting and retaining young PPDs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".