Patients for Patient Safety
Bibliographic record
Abstract
Abstract Unsafe care results in over 2 million deaths per year and is considered one of the world’s leading causes of death. In 2019, the 72nd World Health Assembly issued a call to action, The Global Action on Patient Safety, that called for Member States to democratize healthcare by engaging with the very users of the healthcare system—patients, families, and community members—along with other partners—in the “co-production” of safer healthcare. The WHO’s Patients for Patient Safety (PFPS) Programme, guided by the London Declaration, addresses this global concern by advancing co-production efforts that demonstrate the powerful and important role that civil society, patients, families, and communities play in building harm reduction strategies that result in safer care in developing and developed countries. The real-world examples from the PFPS Programme and Member States illustrate how civil society as well as patients, families, and communities who have experienced harm from unsafe care have harnessed their wisdom and courageously partnered with passionate and forward-thinking leaders in healthcare including clinicians, researchers, policy makers, medical educators, and quality improvement experts to co-produce sustainable patient safety initiatives. Although each example is different in scope, structure, and purpose and engage different stakeholders at different levels, each highlights the necessary building blocks to transform our healthcare systems into learning environments through co-production of patient safety initiatives, and each responds to the call made in the London Declaration, the WHO PFPS Programme, and the World Health Assembly to place patients, families, communities, and civil society at the center of efforts to improve patient safety.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.238 | 0.086 |
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".