Why patient safety is akin to weaving textiles: an International Society for Quality in Health Care perspective on the 7th Global Ministerial Summit on Patient Safety 2025
Bibliographic record
Abstract
The 7th Global Ministerial Summit on Patient Safety, convened in Manila in April 2025, advanced global efforts to reduce harm in healthcare under the theme 'Weaving Strengths for the Future of Patient Safety'. Drawing on the metaphor of textile weaving, the Summit emphasised that safeguarding patients is a collaborative endeavour, requiring integrated action across sectors and systems. The Mandaluyong Declaration reaffirmed commitments to the WHO Global Patient Safety Action Plan (2021-2030) and made four pledges: strengthening global collaboration, advancing leadership and governance, integrating patient safety into disaster preparedness and climate resilience, and building people-centred safety systems. The mission of the International Society for Quality in Health Care (ISQua), a key presence at the Summit, is aligned with these aims, contributing through its White Paper on patient safety, its Green Paper on climate-resilient health systems, and its Person-Centred Care White Paper. The Summit marked a shift from commitment to implementation, recognising the importance of climate change, digital transformation, and equity as integral to safe care. Patient safety now stands as both a global health and environmental priority. ISQua will continue supporting this work through advocacy, standards development, and partnerships, helping to weave patient safety into the daily practice of healthcare worldwide.
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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 | 0.082 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.040 | 0.030 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.033 | 0.064 |
| Insufficient payload (model declined to judge) | 0.008 | 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".