A72 BUILDING A SMARTPHONE APPLICATION FOR COLONOSCOPY PREPARATION USING A PATIENT-CENTERED APPROACH
Bibliographic record
Abstract
Smartphones are daily use instruments that can serve as a powerful reminders to help individuals adhere to colonoscopy attendance and bowel preparation instructions. The objective of this qualitative study was to better understand users’ preferences for the content and features of a smartphone application that supports colonoscopy preparation. Individuals aged 18 or over, English- or French-speaking, with recent colonoscopy and without colorectal cancer were invited to participate in one focus group session at the McGill University Health Centre. Participants were asked to discuss the kinds of Mobile health support tools they might use to help them carry out colonoscopy, the informational content needed to follow through with preparing for colonoscopy, and the information format that would make it easy to use the smartphone application. Discussions were 60–90 minutes, conducted by a trained facilitator using a standardized approach, and audiotaped for subsequent analysis. Nine individuals (2 women, 7 men) attended one of two focus groups. Seven themes were derived from the discussions: colonoscopy preparation, reminders & alerts, application features, information and instructions, data to input, ability to communicate with endoscopy staff, videos. Participants in both focus groups understood the benefits of a smartphone application that included: 1) it ensures patients do the right thing at the right time; 2) it eliminates conflicting and/or fear-inducing information; 3) it can be tailored to individuals’ needs and expectations. Focus groups were conducted to ensure that the smartphone application addresses users’ needs and expectations for information to carry out the colonoscopy. Findings are being used to develop a smartphone application that supports patients prepare for and attend colonoscopy. Department of Medicine, McGill University and the Research Institute of the McGill University Health Centre
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".