MétaCan
Menu
Back to cohort
Record W2169341855 · doi:10.1007/s12160-012-9449-3

Does Questionnaire Distribution Promote Blood Donation? An Investigation of Question–Behavior Effects

2012· article· en· W2169341855 on OpenAlexaboutno aff
Anne van Dongen, Charles Abraham, Robert A. C. Ruiter, I.J.T. Veldhuizen

Bibliographic record

VenueAnnals of Behavioral Medicine · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
FundersNational Institutes of HealthNational Institute for Health and Care Research
KeywordsBlood donorMedicineDonationFamily medicinePopulationHealth psychologyDemographyEnvironmental healthPublic healthImmunologyNursing

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to assess the effectiveness of survey administration as a population-level intervention to increase blood donation. METHODS: Study 1 was a randomized controlled trial of new donors comparing 3,518 who received a questionnaire and 3,490 who did not. Study 2 compared matched, randomly selected samples of active donors; 5,789 received a questionnaire, while 6,000 did not. In both studies, the dependent measure was the proportion of donors who attended a blood donation center to give blood within 6 months of survey posting. Study 3 compared data across five similar trials. RESULTS: No difference in volunteering to give blood was observed between those who did and did not receive a questionnaire among either new or active donors, confirming the findings of two other Canadian trials. CONCLUSIONS: Despite earlier optimistic findings, there is little evidence to suggest that survey administration per se will be effective in increasing blood supplies. Implications for behavior change mechanisms are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.045
GPT teacher head0.341
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Behavioral MedicineSame topicBlood donation and transfusion practicesFrench-language works237,207