Referring Patients to Nurses: Outcomes and Evaluation of a Nurse Flexible Sigmoidoscopy Training Program for Colorectal Cancer Screening
Bibliographic record
Abstract
Colorectal cancer is a significant health burden. Several screening options exist that can detect colorectal cancer at an early stage, leading to a more favourable prognosis. However, despite years of knowledge on best practice, screening rates are still very low in Canada, particularly in Ontario. The present paper reports on efforts to increase the flexible sigmoidoscopy screening capacity in Ontario by training nurses to perform this traditionally physician-performed procedure. Drawing on American, British and local experience, a professional regulatory framework was established, and training curriculum and assessment criteria were developed. Training was initiated at Princess Margaret Hospital and Sunnybrook and Women's College Health Sciences Centre in Toronto, Ontario. (During the study, Sunnybrook and Women's College Health Sciences Centre was deamalgamated into two separate hospitals: Women's College Hospital and Sunnybrook Health Sciences Centre.) Six registered nurses participated in didactic, simulator and practical training. These nurses performed a total of 77 procedures in patients, 23 of whom had polyps detected and biopsied. Eight patients were advised to undergo colonoscopy because they had one or more neoplastic polyps. To date, six of these eight patients have undergone colonoscopy, one patient has moved out of the province and another patient is awaiting the procedure. Classifying the six patients according to the most advanced polyp histology, one patient had a negative colonoscopy (no polyps found), one patient's polyps were hyperplastic, one had a tubular adenoma, two had advanced neoplasia (tubulovillous adenomas) and one had adenocarcinoma. All these lesions were excised completely at colonoscopy. Overall, many difficulties were anticipated and addressed in the development of the training program; ultimately, the project was affected most directly by challenges in encouraging family physicians to refer patients to the program. As health human resource strategies continue to evolve, it is believed that lessons learned from experience make an important contribution to the knowledge of how nontraditional health services can be organized and delivered.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".