Shared Decision Making in Colorectal Cancer Screening: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) is easily detectable with screening, but due to a variety of factors, over a third of eligible people do not get screened. One barrier to people getting adequate screening is confusion about what type of test to use. Shared decision making (SDM) is a way for the clinician to help the patient decide about whether to get screened for CRC and if so, which test to use. This scoping review examines literature about shared decision making in CRC screening to identify key factors affecting a patient's decision. METHODS: We identified studies published from January 1, 2010 through May 18, 2024 through searching 4 databases. Studies were peer reviewed publications in English that investigated the role of shared decision making in colorectal cancer screening in average risk adults, aged 45 -75, in the United States and Canada. Two independent researchers screened all titles and abstracts for eligibility and reviewed all included full text articles. The included studies were examined for themes affecting a patient's choice of screening test. RESULTS: Of the 5672 unique records identified, we included 28 studies in this scoping review. Four themes emerged as being important to the process of shared decision making in colon cancer screening: knowledge of specific aspects of each test, clinician recommendation, acknowledgment of strong emotions surrounding screening, and importance of external factors in decisions around screening. Patients relied on family members for information about screening and made decisions about screening using this information as well as logistics of the test chosen. CONCLUSION: Future work can focus on the importance of external factors in screening decisions and recognizing and addressing the real emotions about CRC screening.
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 | 0.038 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".