How do physician assessments of patient preferences for colorectal cancer screening tests differ from actual preferences? A comparison in Canada and the United States using a stated‐choice survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patient preferences can affect colorectal cancer (CRC) screening test use. We compared utility-based preferences for alternative CRC screening tests from a stated-preference discrete-choice survey of the general population and physicians in Canada and the United States. METHODS: General population respondents (Canada, n = 501; US, n = 1087) participated in a survey with 12 choice scenarios and 9 CRC screening test attributes. Physicians (n = 100, both Canada and US) reported expected patient preferences. We estimated relative importance of attributes using bivariate probit regression analysis and calculated willingness-to-pay for various CRC screening tests. RESULTS: In 28 and 31% of scenarios, Canadian and US respondents, respectively, chose no screening over a hypothetical test. Canadian (45%) and US (46%) physicians expected patients to choose no screening more often. For all groups the most important attribute was sensitivity, but physicians' perception of patients' preferences are significantly different from actual preferences. Other key attributes are those related to test performance or the testing process. Fecal DNA, colonoscopy, and virtual colonoscopy were the most preferred tests by all groups, but respondents were willing-to-pay more than physicians predicted. CONCLUSION: Physicians' perception of patients' preferences are quite different from those of the general population. However, among general population and physicians, Canadian and US preferences were similar.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it