Impact of health literacy on shared decision making for prostate‐specific antigen screening in the United States
Bibliographic record
Abstract
BACKGROUND: Current guidelines endorse shared decision making (SDM) for prostate-specific antigen (PSA) screening. The relationship between a patient's health literacy (HL) and SDM remains unclear. In the current study, the authors sought to identify the impact of HL on the rates of PSA screening and on the relationship between HL and SDM following the 2012 US Preventive Services Task Force recommendations against PSA screening. METHODS: Using data from the 2016 Behavioral Risk Factor Surveillance System, the authors examined PSA screening in the 13 states that administered the optional "Health Literacy" module. Men aged ≥50 years were examined. Complex samples multivariable logistic regression models were computed to assess the odds of undergoing PSA screening. The interactions between HL and SDM were also examined. RESULTS: A weighted sample of 12.249 million men with a rate of PSA screening of 33.4% were identified. Approximately one-third self-identified as having optimal HL. Rates of PSA screening were found to be highest amongst the highest HL group (42.2%). Being in this group was a significant predictor of undergoing PSA screening (odds ratio, 1.214; 95% confidence interval, 1.051-1.403). There was a significant interaction observed between HL and SDM (P for interaction, <.001) such that higher HL was associated with a lower likelihood of undergoing PSA screening when SDM was present. CONCLUSIONS: In the uncertain environment of multiple contradictory screening guidelines, men who reported higher levels of HL were found to have higher levels of screening. The authors demonstrated that increased HL may reduce the screening-promoting effect of SDM. These findings highlight the dynamic interplay between HL and SDM that should inform the creation and promulgation of SDM guidelines, specifically when considering patients with low HL.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".