Perceptions and Usability of PREVENTION: A Breast Cancer Risk Assessment e-Platform
Bibliographic record
Abstract
Background: The PREVENTION e-platform was developed to provide accessible and evidence-based health information tailored to different Breast Cancer (BC) risk levels. The demonstration study objectives were to (1) assess the usability and perceived impact of PREVENTION on women with assigned hypothetical BC risk levels (i.e., near population, intermediate or high) and (2) explore perceptions and recommendations for e-platform improvement. Methods: Thirty women with no history of cancer were recruited through social media, commercial centers, health clinics, and community settings in Montreal, Qc, Canada. Participants accessed e-platform content tailored to their assigned hypothetical BC risk level, and then completed study e-questionnaires including the user Mobile Application Rating Scale (uMARS), an e-platform quality scale (i.e., in terms of engagement, functionality, aesthetics, and information). A subsample (n = 18) was randomly selected for an individual follow-up semi-structured interview. Results: The e-platform overall quality was high, with mean M = 4.01 (out of 5) and SD = 0.50. A total of 87% (n = 26) agreed or strongly agreed that PREVENTION increased their knowledge and awareness of BC risk, and 80% would recommend it to others while reporting likelihood of following lifestyle recommendations to decrease their BC risk. Follow up interviews indicated that participants perceived the e-platform as a trusted source of BC information and a promising means to connect with peers. They also reported that while the e-platform was easy to navigate, improvements were needed for connectivity, visuals, and the organization of scientific resources. Conclusion: Preliminary findings support PREVENTION as a promising means to provide personalized BC information and support. Efforts are underway to further refine the platform, assess its impact in larger samples and gather feedback from BC specialists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".