Medical Safety and Community Practice: Necessary Elements and Barriers to Implement a Safety Learning System
Bibliographic record
Abstract
A safety learning system (SLS) is a system that monitors patient safety incident information and analyzes it to develop and implement improvement strategies to increase patient safety. The purpose of this paper is to discuss the necessary elements of a community-based family medicine practice SLS in Alberta Health Services - Calgary zone, and barriers to, and facilitators of, the implementation of this system. An SLS was developed in the research program Medical Safety in Community Practice. To determine the elements necessary to implement an SLS in community-based family medicine practice, we performed a comprehensive literature review, internal investigator discussions and internal investigator and external stakeholder reviews of key design elements. The system is currently being implemented and tested in community-based family practices as part of the program. Steps identified for implementation: included determining key design elements including creating a website and ascertaining a classification system or taxonomy; developing recruitment strategies; establishing an incident analysis methodology; building a knowledge translation strategy; and pursuing sustainability. These elements produced an SLS that is easily incorporated into community-based family medicine clinics.
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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 | 0.062 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".