Women’s perceptions of PERSPECTIVE: a breast cancer risk stratification e-platform
Bibliographic record
Abstract
BACKGROUND: Breast cancer risk stratification categorizes a woman's potential risk of developing the disease as near-population, intermediate, or high. In accordance, screening and follow up for breast cancer can readily be tailored following risk assessment. Recent efforts have focussed on developing more accessible means to convey this information to women. This study sought to document the relevance of an informational e-platform developed for these purposes. OBJECTIVE: To begin to assess a newly developed breast cancer risk stratification and decision support e-platform called PERSPECTIVE (PErsonalised Risk Stratification for Prevention and Early deteCTIon of breast cancer) among women who do not know their personal breast cancer risk (Phase 1). Changes (pre- and post- e-platform exposure) in knowledge of breast cancer risk and interest in undergoing genetic testing were assessed in addition to perceptions of platform usability and acceptability. METHODS: Using a pre-post design, women (N = 156) of differing literacy and education levels, aged 30 to 60, with no previous breast cancer diagnosis were recruited from the general population and completed self-report e-questionnaires. RESULTS: Mean e-platform viewing time was 18.67 min (SD 0.65) with the most frequently visited pages being breast cancer-related risk factors and risk assessment. Post-exposure, participants reported significantly higher breast cancer-related knowledge (p < .001). Increases in knowledge relating to obesity, alcohol, breast density, menstruation, and the risk estimation process remained even when sociodemographic variables age and education were controlled. There were no significant changes in genetic testing interest post-exposure. Mean ratings for e-platform acceptability and usability were high: 26.19 out of 30 (SD 0.157) and 42.85 out of 50 (SD 0.267), respectively. CONCLUSIONS: An informative breast cancer risk stratification e-platform targeting healthy women in the general population can significantly increase knowledge as well as support decisions around breast cancer risk and assessment. Currently underway, Phase 2, called PERSPECTIVE, is seeking further content integration and broader implementation .
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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".