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Record W4411793580 · doi:10.1177/13591053251346387

Identifying and overcoming barriers and facilitators to blood donation in young adults using the theoretical domains frameworks

2025· article· en· W4411793580 on OpenAlexaboutno aff
Velina Hristova, Freya Mills, Ivo Vlaev

Bibliographic record

VenueJournal of Health Psychology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionDonationLogistic regressionIntervention (counseling)Theory of planned behaviorBlood donorQuarter (Canadian coin)PsychologyMedicineOrgan donationSocial supportFamily medicineSocial psychologyTransplantationNursingControl (management)SurgeryComputer science

Abstract

fetched live from OpenAlex

This study applied the Theoretical Domains Framework (TDF) to identify barriers and facilitators to blood donation among young adults in the UK. A total of 195 individuals (aged 18-29) completed an online survey covering 14 TDF domains, with non-donors offered the chance to register as donors. Binary logistic regression analysis revealed that Knowledge, Beliefs about capabilities and Emotion were the most significant predictors of current donation status. Although nearly half of the non-donors expressed interest in registering as donors, only about a quarter completed the registration when provided with a link. The TDF proved to be an effective framework for understanding the psychological and behavioral factors influencing donation decisions. Based on these findings, targeted intervention strategies were suggested using the Behavior Change Wheel (BCW). These approaches emphasize digital engagement, aligning with the online behaviors and social influences that shape young adults' decision-making. Further research is needed to implement and evaluate these interventions, comparing their effectiveness against current NHS Blood and Transplant (NHSBT) campaigns.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.353
Teacher spread0.336 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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