Building a Safety and Quality Culture in Healthcare: Where It Starts
Bibliographic record
Abstract
Healthcare in Canada underachieves stakeholders' expectations for safe, high-quality care. The authors maintain that a common understanding of, and vision for, what is required to achieve improved outcomes for patients is missing. Educating tomorrow's healthcare professionals is paramount to address this critical shortfall. However, healthcare educational institutions must themselves break out of a 20th-century paradigm of viewing healthcare safety and quality as functions of individual healthcare providers rather than as properties of the clinical micro- and meso-systems within which they function and are a part. Canadian healthcare systems are ailing; like treating a sick patient, interventions should be grounded on a solid understanding of anatomy (structure) and physiology (function). The Healthcare Encounter Safety and Quality Model (HESQM) highlights the structures underlying healthcare delivery and the key system functions required to achieve safe, high-quality care. The model has been used to frame the University of Calgary Faculty of Medicine's educational strategy for achieving safer, higher-quality care. The HESQM is based on leadership - leaders whose decisions and actions are guided by core safety and quality principles. Today's and especially tomorrow's healthcare leaders require a common understanding of how to achieve higher-performing healthcare systems; it is the responsibility of Canada's post-secondary institutions to deliver it.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.040 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 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".