Factors explaining the intention to give blood among the general population
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this study was to identify factors explaining the intention to donate blood. MATERIALS AND METHODS: A random sample of 4000 respondents drawn from the general population received a questionnaire by mail. This questionnaire assessed variables as defined by the most prominent social cognitive theories. RESULTS: Overall, the respondents expressed a neutral mean level of intention to give blood in the next 6 months (2.84 on a five-point scale); 56.2% had never given blood in the past. The variables explaining 74% of the variance of intention were: perceived behavioural control (beta = 0.39; P < 0.001); factors facilitating taking action (beta = 0.25; P < 0.001); anticipated regret (beta = 0.16; P < 0.001); moral norm (beta = 0.11; P < 0.001); attitude (beta = 0.08; P < 0.01); level of education (beta = -0.03; P < 0.05); and past experience in giving blood (beta = 0.09; P < 0.001). Nonetheless, the predictive power of perceived behavioural control and moral norm was higher among the ever donors (both at P < 0.01) compared to the never donors, whereas the reverse was observed for attitude (P < 0.05). CONCLUSIONS: People's intentions are mainly determined by perceived barriers and obstacles regarding blood donations. This suggests that promotional strategies should focus on the elimination of barriers to action as well as the development of a higher perception of control. Also, messages should be adapted to the targeted population, based on their previous blood donation behaviour (i.e. never donors vs. ever donors).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".