An E-Learning Module to Improve Nongenetic Health Professionals’ Assessment of Colorectal Cancer Genetic Risk: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Nongenetic health providers may lack the relevant knowledge, experience, and communication skills to adequately detect familial colorectal cancer (CRC), despite a positive attitude toward the assessment of history of cancer in a family. Specific training may enable them to more optimally refer patients to genetic counseling. OBJECTIVE: The aim of this study was to develop an e-learning module for gastroenterologists and surgeons (in training) aimed at improving attitudes, knowledge, and comprehension of communication skills, and to assess the feasibility of the e-learning module for continued medical education of these specialists. METHODS: A focus group helped to inform the development of a training framework. The e-learning module was then developed, followed by a feasibility test among a group of surgeons-in-training (3rd- and 4th-year residents) and then among gastroenterologists, using pre- and posttest questionnaires. RESULTS: A total of 124 surgeons-in-training and 14 gastroenterologists participated. The e-learning was positively received (7.5 on a scale of 1 to 10). Between pre- and posttest, attitude increased significantly on 6 out of the 10 items. Mean test score showed that knowledge and comprehension of communication skills improved significantly from 49% to 72% correct at pretest to 67% to 87% correct at posttest. CONCLUSIONS: This study shows the feasibility of a problem-based e-learning module to help surgeons-in-training and gastroenterologists in recognizing a hereditary predisposition in patients with CRC. The e-learning led to improvements in attitude toward the assessment of cancer family history, knowledge on criteria for referral to genetic counseling for CRC, and comprehension of communication skills.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".