Gastroenterologist and surgeon perceptions of recommendations for optimal endoscopic localization of colorectal neoplasms
Bibliographic record
Abstract
National consensus recommendations have recently been developed to standardize colorectal tumour localization and documentation during colonoscopy. In this qualitative semi-structured interview study, we identified and contrast the perceived barriers and facilitators to using these new recommendations according to gastroenterologists and surgeons in a large central Canadian city. Interviews were analyzed according to the Consolidated Framework for Implementation Research (CFIR) through directed content analysis. Solutions were categorized using the Expert Recommendations for Implementing Change (ERIC) framework. Eleven gastroenterologists and ten surgeons participated. Both specialty groups felt that the new recommendations were clearly written, adequately addressed current care practice tensions, and offered a relative advantage versus existing practices. The new recommendations appeared appropriately complex, applicable to most participants, and could be trialed and adapted prior to full implementation. Major barriers included a lack of relevant external or internal organizational incentives, non-existing formal feedback processes, and a lack of individual familiarity with the evidence behind some recommendations. With application of the ERIC framework, common barriers could be addressed through accessing new funding, altering incentive structures, changing record systems, educational interventions, identifying champions, promoting adaptability, and employing audit/feedback processes. Future research is needed to test strategies for feasibility and effectiveness.
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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".