Prostate cancer patients’ experience and preferences for acquiring information early in their care
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer patients' information needs are well-described, but little is known about their preferred sources and media for obtaining information. We sought to determine prostate cancer patients' experiences and preferences for acquiring information after diagnosis, a time of high information need. METHODS: Population surveys were conducted in four Canadian provinces in 2014-2015. Each provincial cancer registry surveyed a random sample of prostate cancer patients diagnosed in late 2012. RESULTS: A total of 1366 patients responded across provinces. Respondents most frequently tried to obtain information from their urologist; 86% found that easy and 9% found it difficult. Seventy-nine percent of respondents who saw only a urologist felt well-informed compared to 86% of those who saw both a urologist and a radiation oncologist. Eighty-five percent of respondents wanted printed information; 68% wanted it electronically. Respondents' most frequent barriers to obtaining information from physicians were: not actually having enough time (31%), worrying about having enough time (23%), and worrying about asking too many questions (18%). Their most frequent barriers related to internet/printed information, respectively, were uncertainty about quality (63%/49%) and unclear if personally applicable (56%/49%). Recommended facilitators were having a navigator (85%), providing printed information (85%), and someone to answer questions: in person (90%), by phone (66%), or via email (58%). CONCLUSIONS: Prostate cancer patients want urologists to provide them with information and are more likely to report being informed if they see both a urologist and a radiation oncologist. Optimal information provision requires that it be provided both on the internet and in print.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".