A Mobile App to Increase Informed Decisions About Colorectal Cancer Screening Among African American and Caucasian Women
Bibliographic record
Abstract
At this time, there are no interactive mobile apps designed to increase informed decisions about colorectal cancer screening among women. Colorectal cancer is the third leading cause of cancer death among women. The study's purpose was to explore the usability, acceptability, and satisfaction with a mobile app designed to increase colorectal cancer screening informed decisions among 50- to 64-year-old women. Using previous research, an interactive mobile app to increase informed decisions about colorectal cancer screening was developed and pilot tested among African American and Caucasian women (N = 41). In total, 80.6% of women strongly agree/agreed that the mobile app made them think about colorectal cancer screening, 83.8% strongly agree/agreed that the mobile app provided enough information to make a decision about colorectal cancer screening, and 86.1% strongly agree/agreed that the mobile app could help them talk to their provider about colorectal cancer screening. Participants (63.2%) identified family/spouse as who they would talk to about their colorectal cancer screening decision. Participants found the mobile app easy to use and useful in making colorectal cancer screening decisions. Social support is important when making decisions about colorectal cancer screening. Healthcare professionals need new strategies, such as mobile apps, that engage patients, have the potential to increase patient-provider communication, and increase colorectal cancer screening adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".