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Record W2250379366 · doi:10.1016/j.tmrv.2015.12.001

Why Do Blood Donors Lapse or Reduce Their Donation's Frequency?

2015· article· en· W2250379366 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransfusion Medicine Reviews · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBlood donorDonationContext (archaeology)MedicineDemographicsBlood collectionDemographyBlood donationsWhole bloodFamily medicineSurgeryImmunologyEmergency medicineBiologyPolitical science

Abstract

fetched live from OpenAlex

Finding effective ways to retain blood donors is crucial. This study seeks to compare, in a context of a voluntary and nonremunerated system, donor demographics and deterrents to blood donation among plasma/platelet donors (PPDs), regular whole blood donors (WBDs), and lapsed whole blood donors (LWBD). Among 1879 participants to a survey on motivations, time use, and blood donation, 207 WBDs (26%) and 148 PPDs (31%) said that they reduced their donation frequency over the last 5 years. Participants to this survey also included 609 LWBDs, who did not donate in the past 5 years. We asked about reasons why they reduce or cease to donate blood and demographic variables. χ(2) Tests were completed to determine which deterrents stand out across the 3 blood donor groups. The deterrent indicating the highest percentage was "time constraints related to work or studies" (43% for all respondents). Comparison of WBDs, LWBDs, and PPDs shows that results for 7 deterrents were statistically different between the 3 groups. Obstacles to donating blood also vary based on sex, age (life course), and level of education. Blood collection agencies should consider developing new retention strategies tailored to blood donors, taking into account the specific profiles of female/male donors, events that typically occur at various stages of life, and particular challenges associated with differences in levels of education.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.086
GPT teacher head0.307
Teacher spread0.221 · 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