Feasibility of Using a Computer-assisted Intervention to Enhance the Way Women With Breast Cancer Communicate With Their Physicians
Bibliographic record
Abstract
This study was conducted to evaluate the feasibility of using a computer intervention to enhance communication between healthcare professionals and women with breast cancer. Additional aims were to measure the extent to which women achieved their preferred decisional roles and satisfaction with the clinical medical appointment. This two-arm randomized clinical trial design included a convenience sample of 749 women with breast cancer attending 3 urban Canadian outpatient oncology clinics. Most women were older than 50 years and had a high school diploma or greater (57%). Women in the control group completed measures of decision preference before their clinic appointments. Women in the intervention group were encouraged to use the information and decision preference profiles generated by the computer program at their clinic appointments. Levels of involvement in decision making and satisfaction were measured after the clinic appointments. Results showed that although the majority of women in both groups did assume their preferred roles in decision making, a significantly higher proportion of women in the intervention group reported playing a more passive role than originally planned. Both groups reported high satisfaction levels. Future research is required to study how this computer intervention could be used by clinicians to provide information and decision support to these women.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".