Early Feedback for the Development of a Novel Brief Colon Cancer Screening Decision Aid for Adults ≥75 years at Risk for Limited Health Literacy: A Pilot Study
Bibliographic record
Abstract
IntroductionAchieving health literacy is a primary goal of Healthy People 2030 due to the increasing recognition of its role to improve the health and well-being of all populations. Shared decision-making (SDM), a recognized process between patients and health care providers to discuss which health care decision is best for the patient considering the pros and cons, patient preferences, and circumstances, can improve health outcomes. Specifically, SDM can increase patient knowledge and the quality of decision-making, resulting in patients feeling more empowered, demonstrating less decisional regret, and more motivation. Yet, limited health literacy (LHL) can hinder a patient's ability to engage in the SDM process. Patients' ability to engage in SDM can be helped by improving health literacy levels, and by the suitability of the tools available to support them. Decision aids (DA) are educational tools that can help with SDM. SDM provides patients with the necessary skills, which, when paired with DAs designed with and for populations with LHL, can improve communication with health care providers.MethodsGuided by elements of the Ottawa Decision Framework and principles of human-centered design, in this retrospective study we aimed to develop a novel and current brief colon cancer screening DA, "Making a Decision: Should I Stop or Continue Colon Cancer Screening - Ages 75-85," based on feedback from adults ≥75 years at risk for LHL in two focus groups and a comprehensive health literacy demand assessment of the "Making a Decision About Colon Cancer Screening" using four tools to determine its readability, understandability, and actionability.ResultsFindings include a DA that was viewed favorably by older adult participants who were at risk for LHL.ConclusionsWith feedback from older adults at risk for LHL, we have developed a DA that can be tested in a larger randomized control 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".