There Is No News Like Bad News: Women Are More Remembering and Stress Reactive after Reading Real Negative News than Men
Bibliographic record
Abstract
With the advent of specialized television channels offering 24-hour coverage, Internet and smart phones, the possibility to be constantly in contact with the media has increased dramatically in the last decades. Despite this higher access to knowledge, the impact media exposure has on healthy individuals remains poorly studied. Given that most information conveyed in the media is negative and that upon perception of threat, the brain activates the stress system, which leads to cortisol secretion, we decided to determine how healthy individuals react to media information. Accordingly, we investigated whether reading real negative news (1) is physiologically stressful, (2) modulates one's propensity to be stress reactive to a subsequent stressor and (3) modulates remembrance for these news. Sixty participants (30 women, 30 men) were randomly assigned to either twenty-four real neutral news excerpts or to twenty-four real negative excerpts for 10 minutes. They were then all exposed to a well-validated psychosocial stressor, the Trier Social Stress Test (TSST), which consists of an anticipation phase of 10 minutes and a test phase of 10 minutes. A total of eight salivary cortisol samples were collected, at 10-minutes intervals, throughout the experimental procedure. One day later, a free recall of the news was performed. Results showed that although reading negative news did not lead to change in cortisol levels (p>0.05), it led to a significant increase in cortisol to a subsequent stressor in women only (p<0.001). Also, women in the negative news condition experienced better memory for these news excerpts compared to men (p<0.01). These results suggest a potential mechanism by which media exposure could increase stress reactivity and memory for negative news in women.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".