A111 THE CANADA-GLOBAL RATING SCALE: USE OF AN INNOVATION LEARNING COLLABORATIVE METHODOLOGY TO GUIDE PROVINCIAL IMPLEMENTATION IN ALBERTA
Bibliographic record
Abstract
Abstract Background The Canada-Global Rating Scale (C-GRS) is a web-based, patient centered endoscopy quality improvement tool. It assesses the quality of the services an endoscopy unit provides in two dimensions: clinical quality and the quality of the patient experience. It also allows each endoscopy unit to choose priority areas for future QI activities. Scores are submitted twice a year to a centralized website by the local endoscopy site’s C-GRS working group. Uptake of the C-GRS in Alberta has been historically poor with only 22/50 sites submitting a C-GRS survey in 2016. A provincial C-GRS project team was formed in 2018 to spearhead provincial implementation of the C-GRS. Alberta Health Services approved a C-GRS policy in 2020 mandating regular use of the C-GRS in all provincial endoscopy units. Aims The purpose of this project is to describe a process of focused C-GRS implementation using Innovation Learning Collaborative (ILC) methodology. Methods An ILC is a process meant to drive clinical pathway practice changes to achieve system-wide improvements. Inter-professional teams meet at least three times over a 12–18 month period at Learning Sessions to share successes, learnings, resources and data. A balanced scorecard (Figure 1) is used to track C-GRS progression and regression. Action Period meetings are held in between the Learning Sessions to help build collaboration and support the teams. Results The first of three in-person ILC Learning Sessions was successfully held on November 29, 2019. 37 out of 50 sites in Alberta attended. Each site committed to working on up to 6 C-GRS descriptors during the course of the ILC. An updated scorecard is provided after each C-GRS cycle. An average of 25 sites attended Action Period meetings to report on progress and to share learnings with other sites. 44 endoscopy sites submitted a follow-up C-GRS survey in the spring 2020 cycle (an increase of 22 from 2016). 84% of sites demonstrated improvement with the average number of items improved at 5.1 Conclusions Use of ILC methodology with a balanced scorecard approach can achieve system level improvement within a relatively short time frame. Funding Agencies None
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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 | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".