Core competencies for patient safety research: a cornerstone for global capacity strengthening
Bibliographic record
Abstract
BACKGROUND: Tens of millions of patients worldwide suffer disabling injuries or death every year due to unsafe medical care. Nonetheless, there is a scarcity of research evidence on how to tackle this global health priority. The shortage of trained researchers is a major limitation, particularly in developing and transitional countries. OBJECTIVES: As a first step to strengthen capacity in this area, the authors developed a set of internationally agreed core competencies for patient safety research worldwide. METHODS: A multistage process involved developing an initial framework, reviewing the existing literature relating to competencies in patient safety research, conducting a series of consultations with potential end users and international experts in the field from over 35 countries and finally convening a global consensus conference. RESULTS: An initial draft list of competencies was grouped into three themes: patient safety, research methods and knowledge translation. The competencies were considered by the WHO Patient Safety task force, by potential end users in developing and transitional countries and by international experts in the field to be relevant, comprehensive, clear, easily adaptable to local contexts and useful for training patient safety researchers internationally. CONCLUSIONS: Reducing patient harm worldwide will require long-term sustained efforts to build capacity to enable practical research that addresses local problems and improves patient safety. The first edition of Competencies for Patient Safety Researchers is proposed by WHO Patient Safety as a foundation for strengthening research capacity by guiding the development of training programmes for researchers in the area of patient safety, particularly in developing and transitional countries, where such research is urgently needed.
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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 | 0.253 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".