Understanding Canadian student motivations and beliefs about giving blood
Bibliographic record
Abstract
BACKGROUND: A better understanding of Canadian blood donor beliefs and motivations is needed to develop targeted interventions. Recruiters must know how motivation variables and correlation patterns differ with donor experience and sex. STUDY DESIGN AND METHODS: Data addressing reasons for donating, statements about the blood supply, beliefs about donation consequences, and reasons for avoiding donation were collected from 450 undergraduates. Principal components analysis was used to investigate the underlying factorial structure of each domain. Men-women and donor-nondonor differences were explored with multivariate analysis of variance techniques. RESULTS: A bivariate model better represented donor beliefs than did a bipolar conceptualization. Negative beliefs distinguished donors and nondonors more so than did positive factors. Altruism dominated reasons for donating, whereas logistic factors accounted for the most variance in donation avoidance. Women were more concerned about adverse physical consequences, and nondonors expressed higher levels of groundless donation-related fears. CONCLUSION: Recruiters should consider the sex and donation experience of targets when they develop recruitment and retention strategies. Education programs aimed at overcoming fears and heightening awareness of need are recommended, as are operational improvements aimed at reducing barriers posed by time and inconvenience.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".