Identifying Mediators of Intervention Effects Within a Randomized Controlled Trial to Motivate Cancer Genetic Risk Assessment Among Breast and Ovarian Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: A theory-guided Tailored Counseling and Navigation (TCN) intervention successfully increased cancer genetic risk assessment (CGRA) uptake among cancer survivors at increased risk of hereditary breast and ovarian cancer (HBOC). Understanding the pathways by which interventions motivate behavior change is important for identifying the intervention's active components. PURPOSE: We examined whether the TCN intervention exerted effects on CGRA uptake through hypothesized theoretical mediators. METHODS: Cancer survivors at elevated risk for HBOC were recruited from three statewide cancer registries and were randomly assigned to three arms: TCN (n = 212), Targeted Print (TP, n = 216), and Usual Care (UC, n = 213). Theoretical mediators from the Extended Parallel Process Model, Health Action Planning Approach, and Ottawa Decision Support Framework were assessed at baseline and 1-month follow-up; CGRA uptake was assessed at 6 months. Generalized structural equation modeling was used for mediation analysis. RESULTS: The TCN effects were most strongly mediated by behavioral intention alone (β = 0.49 and 0.31) and by serial mediation through self-efficacy and intention (β = 0.041 and 0.10) when compared with UC and TP, respectively. In addition, compared with UC, the TCN also increased CGRA through increased perceived susceptibility, knowledge of HBOC, and response efficacy. CONCLUSIONS: Risk communication and behavioral change interventions for hereditary cancer should stress a person's increased genetic risk and the potential benefits of genetic counseling and testing, as well as bolster efficacy beliefs by helping remove barriers to CGRA. System-level and policy interventions are needed to further expand access.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".