Identifying and overcoming barriers and facilitators to blood donation in young adults using the theoretical domains frameworks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".