How do people become plasma and platelet donors in a VNR context?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it