Breast Cancer Family History and Behavioral Health Intentions: An Esteem-Relevant Mechanism Informed by the Terror Management Health Model
Bibliographic record
Abstract
The terror management health model (TMHM) offers a framework to investigate how concerns about mortality can motivate health-related behaviors through actions that bolster self-esteem. This framework may be especially useful for examining how a family history of breast cancer influences preventative breast health behaviors. Women with no family history, a family history where a family member survived breast cancer, and those who lost a family member to the disease were recruited to participate in one of two preregistered online studies. Participants completed measures of perceived susceptibility, associations of breast cancer with death, breast health esteem, and behavioral breast health intentions. In both studies, the effect of family history on behavioral intentions was serially mediated by susceptibility perceptions, breast cancer-death association, and feelings of esteem related to breast health behaviors. There were no effects of priming mortality. Taken together, the results suggest that both susceptibility perceptions and death associations are critical for encouraging breast health behaviors among women with family history, and this works through a mechanism relevant to self-esteem. Interventions may be more effective when they emphasize the esteem value of breast health behaviors for individuals at increased risk.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".