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Record W4384819304 · doi:10.1111/vox.13496

What influences decisions to donate plasma? A rapid review of the literature

2023· review· en· W4384819304 on OpenAlexaff
Mary Berger, Adam Easterbrook, Kelly Holloway, Dana V. Devine, Nick Bansback

Bibliographic record

VenueVox Sanguinis · 2023
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCanadian Blood ServicesUniversity of TorontoUniversity of British ColumbiaCentre for Advancing Health Outcomes
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Plasma has become an essential ingredient for various medical treatments. Many blood collection agencies rely on voluntary non-remunerated donation when collecting plasma, but at present many do not collect sufficient plasma to meet domestic demands. This rapid review sought to explore the factors that have been found to influence people's decisions to donate plasma to inform future research. METHODS: Searches were conducted in PubMed, PsycINFO, Social Sciences Citation Index and CINAHL for peer-reviewed journal articles that discussed plasma donation and the factors associated with donor behaviour. Pertinent information from included articles was extracted and arranged in themes. RESULTS: In total, 33 articles were included in this review. Three main themes were identified by the authors. The first focused on site-level factors related to blood collection agencies' engagement with plasma donors and their influence on plasma donation experiences. The second theme considered how individual characteristics and experiences influence willingness to donate plasma. The third theme examined social and cultural-level factors, such as how social networks and community shape perceptions and experiences with donation. CONCLUSION: Our findings suggest that the current understanding of plasma donation is focused mainly on converting whole blood donors and also centres on individual-level factors to donation. Further research must examine what factors attract non-whole blood donors to become plasma donors, focusing on broader social-level influences. This review will inform policies and interventions for blood collection agencies to increase plasma donors.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.080
GPT teacher head0.344
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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