SHAPING QUALITY THROUGH VISION, STRUCTURE, AND MONITORING OF PERFORMANCE AND QUALITY INDICATORS: IMPACT STORY FROM THE QUEBEC TRAUMA NETWORK
Bibliographic record
Abstract
OBJECTIVES: The Quebec Trauma Care Continuum (TCC) was initiated in 1991 with the objective of providing accessible, continuous, efficient, and high quality services for all injury cases in the province. METHODS: The TCC design relied on three key components: (i) the designation of a network of acute care and rehabilitation facilities with specific mandates and responsibilities; (ii) the elaboration of transfer protocols, standing agreements, and governing structures to ensure fluid and optimal patient flow; and (iii) the close monitoring of several indicators to facilitate the continuous evaluation and improvement of the network. RESULTS: Between 1992 and 2002, in-hospital mortality following major trauma decreased from 51.8 percent to 8.6 percent, followed by an additional 24 percent drop between 1999 and 2012. We also observed a 16 percent decrease in average LOS but no change in the incidence of complications or unplanned readmissions. These changes translate into 186 lives saved per year and cost savings, due to shorter LOS, of 6.3 million CD$ per year. The risk-adjusted incidence of in-hospital mortality following major injury between 2006 and 2012 (7 percent) was the lowest of all Canadian provinces. CONCLUSIONS: Strategic transformation of a network's structure and processes, supported by continuous monitoring of validated quality indicators, can lead to significant and sustainable improvements in clinical outcomes. It is hoped that the Quebec trauma story will inspire other jurisdictions and other healthcare sectors.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".