An experimental study revisiting the link between media attention and breast cancer concern: exploring the role of cognitive fusion
Bibliographic record
Abstract
Research examining the link between media attention and breast cancer concern has been frequently conducted with middle/old-age women, even though young women (<40 years old) have been overrepresented media stories about breast cancer. Accordingly, little is known about young women's emotional reactions to breast cancer media messages and the psychological factors modulating such reactions. This study examined the impact of breast cancer media messages and cognitive fusion on negative affect, fear of breast cancer (FBC), and perceived susceptibility to breast cancer. 207 young women were randomly assigned to watch a low- or high-threat video about breast cancer. A MANCOVA revealed that participants who viewed the high-threat video reported greater negative affect and perceived susceptibility, but not FBC; however, participants in both conditions showed moderate/high FBC. Correlational analyses and a MANOVA showed that participants reporting higher cognitive fusion reported higher negative affect across conditions, as well as higher FBC in the high-threat condition. Taken together, these results suggest that young women may show habituation to alarmist media messages, but may nonetheless construe breast cancer as a significant threat. Moreover, young women showing medium/high cognitive fusion seem more likely to show heightened concern upon exposure to alarmist media messages about breast cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".