ISQUA16-2683IMPROVING THE QUALITY AND SAFETY OF THE CONSULTATION AND REFERRAL PROCESS: IT'S MORE THAN PROFESSIONALISM AND COLLEGIALITY
Bibliographic record
Abstract
Quality Referral Evolution (QuRE) is a collaborative initiative to make education and support for quality consultation/referral education part of the postgraduate residency training programs in Alberta. QuRE is organized as well for accredited self-study for practicing physicians and surgeons in the province. The QuRE program intends to improve the quality of communications in both the request and response to the consultation/referral process. The provision of better and timelier access to care for Albertans coupled with enhanced and more effective interdisciplinary communication will increase the quality and safety of health services. The QuRE Working Group was established with representatives from Alberta Health Services (AHS), the University of Calgary and the University of Alberta. AHS is the single health authority for the Province of Alberta. AHS delivers medical care on behalf the Government of Alberta's Ministry of Health through 400 facilities (hospitals, clinics, continuing care/mental health facilities and community health sites). Family practice/primary care services are delivered through a network of 43 Primary Care Networks (PCNs) and non-PCN family physicians.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.021 |
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".