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.
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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads 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".