The Endoscopy Global Rating Scale – Canada: Development And Implementation of a Quality Improvement Tool
Bibliographic record
Abstract
BACKGROUND: Increasing use of gastrointestinal endoscopy, particularly for colorectal cancer screening, and increasing emphasis on health care quality highlight the need for endoscopy facilities to review the quality of the service they offer. OBJECTIVE: To adapt the United Kingdom Global Rating Scale (UK-GRS) to develop a web-based and patient-centred tool to assess and improve the quality of endoscopy services provided. METHODS: Based on feedback from 22 sites across Canada that completed the UK endoscopy GRS, and integrating results of the Canadian consensus on safety and quality indicators in endoscopy and other Canadian consensus reports, a working group of endoscopists experienced with the GRS developed the GRS-Canada (GRS-C). RESULTS: The GRS-C mirrors the two dimensions (clinical quality and quality of the patient experience) and 12 patient-centred items of the UK-GRS, but was modified to apply to Canadian health care infrastructure, language and current practice. Each item is assessed by a yes⁄no response to eight to 12 statements that are divided into levels graded D (basic) through A (advanced). A core team consisting of a booking clerk, charge nurse and the physician responsible for the unit is recommended to complete the GRS-C twice yearly. CONCLUSION: The GRS-C is intended to improve endoscopic services in Canada by providing endoscopy units with a straightforward process to review the quality of the service they provide.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".