Innovating for impact: Standards and assessments as a catalyst for safer, integrated, people-centred care
Bibliographic record
Abstract
In today's evolving healthcare landscape, leaders must innovate and collaborate to ensure safe, accessible, and high-quality care. Addressing complex issues like climate change, workforce resiliency crises, and the erosion of public trust, alongside equity and inclusive services, is critical. This article offers insights into the role of quality standards and assessment programs as catalysts for innovation and future collective impact. Using examples from Health Standards Organization and Accreditation Canada, it illustrates how these levers of change enable leaders to improve patient and workforce safety, advance integrated care, and strengthen the health of communities. Embracing people-centred and evidence-informed solutions to enable new mindsets and ways of working lays the foundation for sustainable, resilient learning health systems.
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 | 0.164 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.052 |
| Scholarly communication | 0.036 | 0.036 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".