Patterns of use and patient perceptions of a decision support software tool for men with early stage prostate cancer
Bibliographic record
Abstract
Computer Assisted Patient Decision Aids (CAPtDA) are important tools to address informed decision making. This parallel mixed methods study described patterns of use of a CAPtDA among men with early stage prostate cancer and explored their perceptions of a CAPtDA and its role in their decision-making process. Men (N=56) with early stage prostate cancer, seeking consultations for surgery and/or radiation therapy at Fox Chase Cancer Center, were recruited by telephone. Those who consented completed a background questionnaire prior to their initial treatment consult. Variables included demographics, decisional factors (such as decision-making style, treatment preference, stage of decision making, Ottawa decisional conflict) and health communication factors (health literacy and computer facility). The CAPtDA had embedded web log tracking capabilities. Men were also asked to participate in an in-depth qualitative interview within 2-4 weeks of their consult visit to explore their perceptions of the software. Twenty five men participated (14 surgical consult patients and 11 radiation consult patients). Specific CAPtDA components were more highly utilized while other components were rarely used. The Men's Stories, with actual men's stories about their diagnosis, treatment decision and challenges, was viewed by 77% of the men and they spent almost half of their time (46%) here. In contrast, the Notebook, which is the values clarification tool, was viewed by only 4 men and they spent about one minute in this section. Men with lower levels of health literacy spent more time in the Men's Stories than men with higher levels of literacy. However, literacy level was not associated with multiple uses and men reported that the content was easy to understand regardless of health literacy level. Those with higher decisional conflict spent more time overall and those who were less confident in their treatment choice were less likely to use it again. Fifteen percent of the sample was minority, but the drop-off rate in participation in the in-depth interviews among minorities and those with limited literacy was dramatic. Opening this "black box" showed different patterns of use and confirmed that not everyone uses it in the same way, or as we intend.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".