The association between physician trust and prostate specific antigen screening: Implications for shared decision making.
Bibliographic record
Abstract
13 Background: Most cancer organizations recommend shared decision making for PSA screening, a process relying on a trusting relationship between patient and physician. The objective of this study was to assess the degree to which an individual’s trust in cancer information from their physician compared to internet-based information impacts the likelihood of receiving PSA-screening. Methods: This was a cross-sectional study (2011-2014) of the Health Information National Trends Survey (HINTS), a survey of people living in the US. The primary exposure was degree of trust in cancer information from participant’s physician (CIP). The secondary exposure was degree of trust in cancer information from the internet (CII). The primary outcome was patient-reported receipt of PSA-screening. The Cochran-Armitage test was used to identify significant trends in the primary outcome, across levels of trust. A multivariable logistic regression model assessed the association between CIP and CII with PSA-screening, adjusted for a priori covariates. Results: Among 5069 eligible respondents, 3,606 (71.1%) reported trusting CIP ‘a lot’, 1,186 (23.4%) ‘some’, 219 (4.3%) ‘a little’, and 58 (1.1%) ‘not at all’. 2,655 (52.4%) men received PSA-screening. The degree of trust in CIP was strongly associated with the likelihood of receiving PSA-screening: among men who reported ‘a lot’ of trust, 54.9% underwent screening, 48.6% ‘some’ trust, 38.4% ‘a little’ trust, and 27.6% among men ‘not at all’ trusting their physician (trend p < 0.0001). The degree to which men trusted CII was also associated with having received PSA-screening (p = 0.005), albeit with an insignificant trend (p = 0.07). After multivariable adjustment, these significant results persisted for degree of CIP trust (vs ‘a lot’: ‘some’ OR 0.80, 95%CI 0.66-0.97; ‘a little’ OR 0.61, 95%CI 0.41-0.90; ‘not at all’ OR 0.33, 95%CI 0.15-0.73), but not for trust in CII. Conclusions: The level of trust an individual has in their physician is strongly associated with undergoing PSA-screening. As rationale implementation of PSA screening requires shared decision-making, the level of physician trust has implications for dissemination of PSA-screening guidelines.
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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 | 0.018 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".