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Record W4313224923 · doi:10.1002/mar.21780

Do incentives work to motivate voluntary blood donation?

2022· article· en· W4313224923 on OpenAlexaff
Yuwen Gong, Ying Dai, Zujun Ma, Jianguo Li

Bibliographic record

VenuePsychology and Marketing · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIncentiveAttractivenessPsychologySocial psychologyDonationBlood donorTurnoverMicroeconomicsMedicineEconomics

Abstract

fetched live from OpenAlex

Abstract Based on previous research on blood donation incentives, we investigated the effectiveness of two incentives—eligibility for free blood transfusions and improving individual credit scores—and explored the psychological mechanisms underlying these effects. We conducted four studies to explore the relationship between incentives and blood donation intention. The results showed that eligibility for free blood transfusions was more effective than improving individual credit scores due to the mediating effect of perceived attractiveness. Meanwhile, improving individual credit scores failed to play an effective role and was significantly lower than eligibility for free blood transfusions due to the mediating effect of perceived threat to freedom. We further found that after adding the moderating variable of involvement, there was no difference between the two incentives due to the weakened mediating effects of perceived threat to freedom and perceived attractiveness in the high‐involvement group. This study establishes two effect paths from incentives to perceived threat to freedom/perceived attractiveness to blood donation intention, explaining the effectiveness of incentives.

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)0.002
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.266
Teacher spread0.251 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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