A Motivational Interviewing Intervention to Promote CRC Screening
Bibliographic record
Abstract
BACKGROUND: Appalachian Kentuckians suffer a disproportionate incidence and mortality from colorectal cancer (CRC) and are screened at lower rates (35%) compared with 47% of Kentuckians. OBJECTIVE: The aim of this study was to evaluate the efficacy of a motivational interviewing intervention delivered by trained Lay Health Advisors on CRC screening. METHOD: Eligible participants recruited from an emergency department (ED) completed a baseline survey and were randomized to either the control or the motivational interviewing intervention provided by Lay Health Advisors. Follow-up surveys were administered 3 and 6 months after baseline. To evaluate potential differences in treatment and control groups, t tests, χ2, and Mann-Whitney U tests were used. RESULTS: At either the 3- or 6-month assessment, there was no difference in the CRC screening by group (χ2 = 0.13, P = .72). There was a significant main effect for the study group in the susceptibility to CRC model; regardless of time, those in the intervention group reported approximately 1-point higher perceived susceptibility to CRC, compared with controls (est. b = 0.68, P = .038). Age and financial adequacy had a significant effect related to CRC screening. Older participants (est. b = 0.09, P = .014) and those who reported financial inadequacy (est. b = 2.34, P = .002) reported more screening barriers. CONCLUSION: This pilot study elucidated important factors influencing the uptake of CRC for an ED transient population and this may be useful in the design of future interventions using motivational interviewing in EDs. IMPLICATIONS FOR PRACTICE: Nurses can provide information about CRC screening guidelines and provide referrals to appropriate screening resources in the community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".