Preventive Health Behaviors in Unaffected High-Risk Women: The Impact of Perceived vs Actual Risk and Preferred Involvement in Decision-Making.
Bibliographic record
Abstract
Abstract Background: While major advances have been made in the diagnosis and treatment of breast cancer, more limited progress has been made in the prevention of the disease. Although several clinical trials have demonstrated the advantages of lifestyle alteration, weight loss and use of anti-estrogens, uptake of such strategies is generally sporadic in women at high risk. It has been particularly challenging to engage even women at very high risk in clinical trials aimed at primary prevention. These decisions may be influenced by several factors, among which perceived risk, actual risk and preferences for participation in preventive decision making may be particularly important. In this project, we seek to describe how perceived risk, actual risk and the preferred level of involvement in the decision jointly impact the risk management intentions and subsequent decisions.Methods: As a regular component of risk assessment process at the High Risk Breast Assessment Clinic (HRBAC) of the Ottawa Regional Women's Breast Health Centre, a detailed questionnaire is administered to women referred to the clinic before their first risk assessment consultation. The questionnaire includes several items required for calculating actual risk (using Gail score) as well as questions concerning health practices (breast screening, clinical breast examination, breast self-examination) and lifestyle practices (weight, height, smoking, alcohol and physical activity). Women's perceived risk is assessed by the following question: “What do you think the likelihood is of you developing breast cancer in your lifetime?”, and women are required to provide a percentage to express their risk. Women are also asked to indicate their intentions about breast cancer prevention and to identify which prevention strategy is the most important to them. Preferred role involvement in decision making is also assessed by the following question: “What role would you like to take in making your decision?”and women are classified as active or passive. Patient's charts are used to obtain information about the prevention decisions that women made 1 year after receiving risk counselling.We hypothesize that risk reduction counselling will weaken the association between perceived risk and prevention decisions and conversely will strengthen the correlation between actual risk and risk reduction option considered by women.Results: Correlational and chi-square analyses of the demographic data, actual(calculated0 and perceived risk, as well as prevention behaviour uptake obtained from 300 patients will be presented. A low correlation between perceived and actual risks is expected before patients receive risk counselling at the clinic. Statistically reliable association between perceived risk and precounselling prevention intentions and a low level association between actual risk and precounselling prevention intentions are also expected. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 1040.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".