Predicting blood donation intention: the importance of fear
Bibliographic record
Abstract
BACKGROUND: Blood donor recruitment remains an important worldwide challenge due to changes in population demographics and shifts in the demand for blood. Various cognitive models help predict donation intention, although the importance of affective deterrents has become increasingly evident. This study aimed to identify fears that predict donation intention, to explore their relative importance, and to determine if self-efficacy and attitude mediate this relationship, thus providing possible targets for intervention. STUDY DESIGN AND METHODS: A total of 347 individuals (269 nondonors and 78 donors) living in Québec responded to questionnaires assessing medical fears, psychosocial factors related to donation intention including the Theory of Planned Behavior (TPB) constructs, anticipated regret, and facilitating factors (i.e., time commitment and rewards). To examine the relative importance of these factors in the context of blood donation, the same questions were also asked about other medical activities that involve salient needle stimuli: flu vaccinations and dental examinations. RESULTS: Medical fears, especially blood-related fears, were significantly associated with donation intention. Bootstrapping tests confirmed that this relation was mediated by attitude and self-efficacy. Underlining the importance of medical fears in the blood donation context, these fears were not associated with attitudes and intentions for dental examinations or flu vaccinations. CONCLUSION: These results suggest that medical fears, especially blood-related fears, play a key role in predicting donation attitudes and intentions. Mediational pathways provide support for interventions to improve donation intentions by addressing specific fears while also improving a donor's belief in his or her ability to manage donation-related fears.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".