Do Primary Care Provider Strategies Improve Patient Participation in Colorectal Cancer Screening?
Bibliographic record
Abstract
OBJECTIVES: Screening rates for colorectal cancer (CRC) remain suboptimal. The impact of provider strategies to enhance screening participation in the population is uncertain. The objective of this study was to determine the effect of provider strategies to increase screening in a single-payer system. METHODS: A population-based survey was conducted in primary care providers (PCPs) linked to patients using administrative data in Ontario, Canada. Patients were due for CRC screening from April 2012 to March 2013. Patients were followed up until 31 March 2014. We determined time to become up-to-date with CRC screening. Cox proportional hazards models examined the association between PCP strategies and uptake of screening, adjusted for physician and patient factors. RESULTS: A total of 717 PCPs and their 147,834 rostered patients due for CRC screening were included. Most physicians employed strategies to enhance screening participation, including electronic medical record use, reminders, generation of lists, audit and feedback reports, or designating staff responsible for screening. No single strategy was strongly associated with screening. For those >1 year overdue, a systematic approach to generate lists of patients overdue for screening was weakly associated with screening uptake (hazard ratio (HR)=1.14, 95% CI: 1.03-1.26, P=0.04 >5 years overdue vs. <1 year overdue). The use of multiple PCP strategies was associated with screening participation (HR=1.27, 95% CI: 1.16-1.39, P<0.0001 for PCPs using 4-5 vs. 0-1 strategies). Practice-based strategies were self-reported. CONCLUSIONS: In practice, while individual PCP strategies have little effect, the use of multiple strategies to enhance screening appears to improve CRC screening uptake in patients.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".