Preparing African American Men to Make Informed Prostate Cancer Screening Decisions: Development and Pilot Testing of an Interactive Online Decision Aid
Bibliographic record
Abstract
BACKGROUND: African American men are at a higher risk of developing and dying from prostate cancer compared to white men. The serum prostate-specific antigen (PSA) screening test has a high risk of false-positive results and overdiagnosis; therefore, it is not routinely recommended. Rather, men are encouraged to make individualized decisions with their medical providers, after being fully informed about its potential benefits, limitations, and risks. OBJECTIVE: This study aimed to describe the development and pilot testing of an interactive Web-based decision aid (DA; Prostate Cancer Screening Preparation [PCSPrep]) for African American men, designed to promote informed decision making for prostate cancer screening. METHODS: Four focus groups (n=33) were conducted to assess men's reactions to DAs developed in prior studies and gather information to modify the content and format. The pilot test employed a pre-posttest evaluation design. A convenience sample of 41 men aged 45-70 years with no history of prostate cancer was recruited from community settings. Participants completed online surveys before and after using PCSPrep that assessed prostate cancer screening knowledge, decision self-efficacy, decisional conflict, and preparation for decision making. RESULTS: Use of PCSPrep was associated with a significant increase in prostate cancer knowledge (49% vs 62% correct responses; P<.001), and men also experienced less decisional conflict (24 vs 15 on a scale of 0-100; P=.008). No changes in self-efficacy about decision making or screening preferences were observed. Most men (81%) reported that using PCSPrep prepared them to make informed decisions in partnership with their provider. CONCLUSIONS: PCSPrep was an acceptable DA that improved men's knowledge, reduced decisional conflict, and promoted the perception of being prepared for shared decision making. Further research is needed to test the DA in a larger randomized trial.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".