Staying informed without a cost: No effect of positive news media on stress reactivity, memory and affect in young adults
Bibliographic record
Abstract
INTRODUCTION: We read, see and hear news from various media sources every day. A large majority of the news is negative. A previous study from our laboratory showed that reading negative news is associated with both increased stress reactivity (measured via the stress hormone cortisol) and recall of the negative news segments in women. OBJECTIVES: The present study investigated the effects of positive news on cortisol stress reactivity, memory and affect using a methodology highly similar to the study on negative news that was previously used by our team. METHODS: Sixty-two healthy participants aged between 18 and 35 years (81% women) were randomly exposed to either positive or neutral news segments, followed by a laboratory stressor. We assessed participants' affect three times during the procedure and measured cortisol in saliva eight times (at 10-minute intervals). Twenty-four hours later, participants were contacted by phone to assess their recall of the news segments. RESULTS: Results showed that exposure to positive news, relative to neutral news, did not modulate participants' cortisol levels in response to the laboratory stressor. Positive news had no impact on memory recall of the news and did not change participants' positive or negative affect. Bayes factors suggested that these nonsignificant results are not attributable to low statistical power. CONCLUSION: Contrary to negative news, positive and neutral news do not modulate stress reactivity, memory and affect. These results suggest that people can stay informed without physiological and psychological costs when the news to which they are exposed adopt a positive or neutral approach.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".