Improving colorectal cancer screening rates using motivational interviewing
Bibliographic record
Abstract
Background and objective: Early diagnosis of colorectal cancer (CRC) through screening is associated with survival rates of more than 90%. Nearly half of American adults are not compliant with recommendations. The purpose of this project is to implement and evaluate an evidenced based protocol utilizing motivational interviewing as an intervention to improve CRC screening rates among a Veteran population. Methods: The project design includes a single session telephone based motivational interviewing session two weeks after receipt of fecal immunochemical test (FIT) for home screening. A motivational interview roadmap was developed to guide the telephone session.Results: All participants were male and 76% had previously completed a CRC screening test. Fourteen percent (n = 7/50) of participants returned their FIT within 2 weeks. Of the 38 participants eligible for telephone based motivational interview 66% (n = 25) were unreachable by telephone and received one or two voicemail messages that stressed the importance of returning the FIT. Of the motivational interviewing recipients, 62% (n = 8/13) successfully returned their FIT. Conclusions: These results provide beginning evidence for the effectiveness of motivational interviewing to improve CRC screening rates. Issues with system processes and healthcare provider behaviors were identified and recommendations for improvement are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".