Are Patient Education Materials about Cancer Screening More Effective When Co-Created with Patients? A Qualitative Interview Study and Randomized Controlled Trial
Bibliographic record
Abstract
Background: Patient education materials (pems) are frequently used to help patients make cancer screening decisions. However, because pems are typically developed by experts, they might inadequately address patient barriers to screening. We co-created, with patients, a prostate cancer (pca) screening pem, and we compared how the co-created pem and a pem developed by experts affected decisional conflict and screening intention in patients. Methods: We identified and used patient barriers to pca screening to co-create a pca screening pem with patients, clinicians, and researchers. We then conducted a parallel-group randomized controlled trial with men 40 years of age and older in Ontario to compare decisional conflict and intention about pca screening after those men had viewed the co-created pem (intervention) or an expert-created pem (control). Participants were randomized using dynamic block randomization, and the study team was blinded to the allocation. Results: Of 287 participants randomized to exposure to the co-created pem, 230 were analyzed, and of 287 randomized to exposure to the expert-created pem, 223 were analyzed. After pem exposure, intervention and control participants did not differ significantly in Decisional Conflict Scale scores [mean difference: 0.37 ± 1.23; 95% confidence interval (ci): -2.05 to 2.79]; in sure (Sure of myself, Understand information, Risk-benefit ratio, or Encouragement) scores (odds ratio: 0.75; 95% ci: 0.52 to 1.08); or in screening intention (mean difference: 0.09 ± 0.08; 95% ci: -0.06 to 0.24]). Conclusions: The effectiveness of the co-created pem did not differ from that of the pem developed by experts. Thus, pem developers should choose the method that best fits their goals and resources.
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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 | 0.042 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".