Activating Knowledge for Patient Safety Practices: A Canadian Academic‐Policy Partnership
Bibliographic record
Abstract
BACKGROUND: Over the past decade, the need for healthcare delivery systems to identify and address patient safety issues has been propelled to the forefront. A Canadian survey, for example, demonstrated patient safety to be a major concern of frontline nurses (Nicklin & McVeety 2002). Three crucial patient safety elements, current knowledge, resources, and context of care have been identified by the World Health Organization (WHO 2009). To develop strategies to respond to the scope and mandate of the WHO report within the Canadian context, a pan-Canadian academic-policy partnership has been established. APPROACH: This newly formed Pan-Canadian Partnership, the Queen's Joanna Briggs Collaboration for Patient Safety (referred throughout as "QJBC" or "the Partnership"), includes the Queen's University School of Nursing, Accreditation Canada, the Canadian Patient Safety Institute (CPSI), the Canadian Institutes of Health Research, and is supported by an active and committed advisory council representing over 10 national organizations representing all sectors of the health continuum, including patients/families advocacy groups, professional associations, and other bodies. This unique partnership is designed to provide timely, focused support from academia to the front line of patient safety. QJBC has adopted an "integrated knowledge translation" approach to identify and respond to patient safety priorities and to ensure active engagement with stakeholders in producing and using available knowledge. Synthesis of evidence and guideline adaptation methodologies are employed to access quantitative and qualitative evidence relevant to pertinent patient safety questions and subsequently, to respond to issues of feasibility, meaningfulness, appropriateness/acceptability, and effectiveness. SUMMARY: This paper describes the conceptual grounding of the Partnership, its proposed methods, and its plan for action. It is hoped that our journey may provide some guidance to others as they develop patient safety models within their own arenas.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".