Social contagion of vasovagal reactions in the blood collection clinic: A possible example of mass psychogenic illness.
Bibliographic record
Abstract
OBJECTIVE: Observing or hearing about illness in another person can lead to reports of similar symptoms. Reports can occasionally be widespread. However, it has been difficult to document whether this is the result of genuine illness or the expression of anxiety with physical terminology. This study examined the effects of being able to see another blood donor experience vasovagal symptoms. METHODS: Data were collected in mobile university blood collection clinics. Bedside research assistants coded whether the donor was able or not able to see another donor being treated for vasovagal symptoms. Dependent variables included subjective vasovagal symptoms indicated on the Blood Donation Reactions Inventory (BDRI) and the need for treatment oneself. Given the population of inexperienced donors, many (26% of the 1,209 participants) were able to see another donor treated for symptoms. RESULTS: Being able to see another donor treated was associated with higher scores on the BDRI and an increased likelihood of treatment for vasovagal symptoms oneself. However, this was limited to non-first-time blood donors, perhaps because of higher levels in first-time donors (ceiling effects) or greater attention to the environment in less "overwhelmed" repeat donors. In general, donors who were able to see another react rated themselves as less relaxed and had smaller increases in heart rate. During the 2-year follow-up, first-time donors who were able to see another react were slower to return to give blood again. CONCLUSIONS: Seeing another donor being treated for symptoms contributed to the vasovagal process in many donors. This environment provides a useful context to study social influences on symptoms and illness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".