The role of cognitive appraisal and worry in BRCA1/2 testing decisions among a clinic population
Bibliographic record
Abstract
Previous studies examining decision making in the context of genetic testing for BRCA1/2 gene mutations have been limited in their reliance on cross-sectional designs, lack of theoretical guidance, and focus on measures of intention rather than actual behavior. Informed by the Health Belief Model and other theories of self-regulation, the present study set out to examine the role of cognitive appraisal and worry in BRCA1/2 testing decisions. A total of 205 women completed baseline questionnaires prior to their genetic counselling appointment. Medical charts were audited to determine testing decisions. Bivariate analyses indicated that perceived severity of being a carrier and perceived benefits and barriers to testing were significantly associated with testing decisions. Perceived benefits remained significant in multivariate analyses. Moreover, multivariate analyses revealed a significant three-way interaction between perceived susceptibility, perceived severity, and worry about being a mutation carrier and testing decisions. Among women high in baseline worry, those high in perceived susceptibility but low in perceived severity were significantly more likely to undergo genetic testing than all other susceptibility/severity combinations (80% vs. 36.2–42.9% range; Wald test = 8.79, p < 0.01). These results support the need for researchers and practitioners to consider how interactions between cognition and worry may influence genetic testing decisions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".