Effectiveness of Behavioural Intervention as Treatment for the Vasovagal Response in Blood Donation
Bibliographic record
Abstract
Background: The experience of a vasovagal reaction during blood donation, with symptoms such as dizziness, weakness, and fainting, contributes to a more negative donation experience and significantly decreases the likelihood of blood donor return. This study investigates the effects of two behavioural interventions on reducing the occurrence of such reactions, applied muscle tension and respiration control, and possible moderation of these effects by sex, BMI, and medical fear. Methods: Six hundred and eleven participants were recruited from Héma-Québec blood drives across Montreal and randomly assigned one of four conditions: applied muscle tension, an anti-hyperventilation respiration control procedure, both techniques, or neither. Following their donation, participants completed the Blood Donations Reactions Inventory and Medical Fears Survey. Analysis focuses on the respiration control and applied tension groups. Results: While donor sex and BMI did not predict the effectiveness of applied muscle tension intervention, results showed that the largest benefit was seen in donors who reported lower levels of medical fears in the respiration control condition group. Limitations/Conclusions: The results are promising in that they suggest that intervention can decrease the risk for vasovagal symptoms in blood donation, though it may not be sufficient to reduce symptoms in donors with high levels of medical fear.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".