Safety learning system development--incident reporting component for family practice
Bibliographic record
Abstract
OBJECTIVE: To determine the required components for developing the reporting components of a safety learning system (SLS) for community-based family practice. METHODS: Multiple databases were searched for all languages for all types of papers related to medical safety in community practice: Books@Ovid, BIOSIS Previews, CDSR, ACP Journal Club, DARE, CCTR, Ageline, AMED, CINAHL, EMBASE, HealthSTAR, Ovid MEDLINE In-Process, Other Non-Indexed Citations, Ovid MEDLINE, PsycINFO, HAPI and PsycBOOKS. A grey literature search was done in Google. RESULTS: The online search identified 190 papers. English abstracts were read and the full papers (or chapters) were retrieved for 90, of which 18 were deemed appropriate. The grey literature search revealed 18 additional papers, and an additional 12 papers were identified from bibliographies of included papers. The common themes identified from the articles became the main consideration for developing an SLS for family practice and include current and past initiatives, system design, incident reporting form and classification system. CONCLUSION: There is a small but growing body of literature concerning the requirements for developing the reporting component of an SLS for family practice. For the reporting component of an SLS to be successful, there needs to be strong leadership, voluntary reporting, legal protection and feedback to reporters.
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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 | 0.110 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".