Health consequences of a death threat: How terrorist attacks impact drinking
Bibliographic record
Abstract
Abstract Terrorist attacks, war, violent acts, and their media coverage remind us of our own mortality, which may provoke stress and coping mechanisms. The terror management health model (TMHM) proposes that even subliminal thoughts about existential threats trigger worldview defense and self‐esteem‐related behaviors. Based on the TMHM, our field experiment (N = 228) examines the impact of a terrorist attack on death‐thought accessibility, the choice between alcoholic and nonalcoholic beverages, and if the impact on this choice is moderated by the importance of alcohol to one's self‐esteem (i.e., alcohol‐based self‐esteem), and the consciousness of the terrorist attack. Results show that thoughts about the terrorist attack increased death‐thought accessibility. The salience of the terrorist attack had no main effect on beverage choice, but alcohol‐based self‐esteem predicted choosing an alcoholic beverage. However, in the unconscious thought condition, participants who had low alcohol‐based self‐esteem and were provoked with death‐related thoughts about terrorism were more likely to choose an alcoholic beverage. In the conscious thought condition, participants who had high alcohol‐based self‐esteem were less likely to choose alcohol. This study suggests that thoughts about terrorism and, therefore, the threat of death, can be provoked in everyday situations and affect substance use behaviors with potentially adverse health consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".