Crafting Appealing Text Messages to Encourage Colorectal Cancer Screening Test Completion: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: mHealth interventions that incorporate text messages have great potential to increase receipt of preventive health services such as colorectal cancer screening. However, little is known about older adult perspectives regarding the receipt of text messages from their health care providers. OBJECTIVE: To assess whether older adults would value and access text messages from their physician's practice regarding colorectal cancer screening. METHODS: We conducted four focus groups with 26 adults, aged 50 to 75 years, who had either recently completed or were overdue for colorectal cancer screening. A trained moderator followed a semistructured interview guide covering participant knowledge and attitudes regarding colorectal cancer screening, potential barriers to colorectal cancer screening, attitudes about receiving electronic communications from a doctor's office, and reactions to sample text messages. RESULTS: Participant responses to three primary research questions were examined: (1) facilitators and barriers to colorectal cancer screening, (2) attitudes toward receiving text messages from providers, and (3) characteristics of appealing text messages. Two themes related to facilitators of colorectal cancer screening were perceived benefits/need and family experiences and encouragement. Themes related to barriers included unpleasantness, discomfort, knowledge gaps, fear of complications, and system factors. Four themes emerged regarding receipt of text messages from health care providers: (1) comfort and familiarity with technology, (2) privacy concerns/potential for errors, (3) impact on patient-provider relationship, and (4) perceived helpfulness. Many participants expressed initial reluctance to receiving text messages but responded favorably when shown sample messages. Participants preferred messages that contained content that was important to them and were positive and reassuring, personalized, and friendly to novice texters (eg, avoided the use of texting shorthand phrases and complicated replies); they did not want messages that contain bad news or test results. They wanted the ability to choose alternative options such as email or phone calls. CONCLUSIONS: Older adults are receptive to receiving cancer screening text messages from health care providers. Sharing sample messages with patients may increase acceptance of this tool in the clinic setting. Supportive tailored text messaging reminders could enhance uptake of colorectal cancer screening by enhancing patient self-efficacy and providing cues to action to complete colonoscopy or fecal occult blood testing.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".