Bringing diversity of experience into decision-making about surgery: developing an app for that
Bibliographic record
Abstract
ObjectiveTo design and pilot a mobile application to support decision-making about surgery for breast cancer patients, drawing from a collection of narratives from a diverse sample of Canadian women that used video/audio recordings (published on www.healthexperiences.ca). BackgroundThis idea came from the results of a larger research project where we collected a wide range of women’s stories about having breast cancer using qualitative research methods and video/audio recording. The women we interviewed for this project said that they felt overwhelmed by the amount of information in the period between diagnosis and surgery, and at the same time they felt they needed more specific information relevant to their needs, personal situation and preferences. They also wanted to learn from other women’s experiences of surgery. This project therefore aims to develop an information tool that can respond to that need by offering information in a more personalized manner. This project is funded by the QBCF [Quebec Breast Cancer Foundation]. MethodsSecondary analysis of existing data. Development of recommender system. Design and evaluation involving patients, clinicians and informatics experts. ResultsWe will present our analysis regarding women’s experiences related to surgery, decision-making, preferences and expectations, and need for experiential information. We will then present the development to date of a personalized information application using a computerized recommender system, including a summary of challenges and opportunities encountered along the way. ConclusionDeveloping reliable, evidence-based tools and electronic applications based on other peoples’ experiences offers a novel approach to support informed healthcare decision-making.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".