Development of a Measure of Patient Safety Event Learning Responses
Bibliographic record
Abstract
OBJECTIVE: To define patient safety event (PSE) learning response and to provide preliminary validation of a measure of PSE learning response. DATA SOURCES: Ten focus groups with front-line staff and managers, an expert panel, and cross-sectional survey data from patient safety officers in 54 general acute hospitals. STUDY DESIGN: A mixed methods study to define a measure of learning responses to patient safety failures that is rooted in theory, expert knowledge, and organizational practice realities. EXTRACTION METHODS: Learning response items developed from the literature were modified and validated in front-line staff and manager focus groups and by an expert panel and second group of external experts. Actual learning responses gleaned from survey data were examined using exploratory factor analyses and reliability analysis. PRINCIPAL FINDINGS: Unique learning response items were identified for minor, moderate, major events, and major near misses by an expert panel. A two-factor model of major event learning response was identified (factor 1=event analysis, factor 2=dissemination/communication of learnings). Organizations engage in greater learning responses following major events than less severe events and, for major events, organizations engage in more factor 1 responses than factor 2 learning responses. CONCLUSIONS: Eleven to 13 items can measure learning responses to PSEs of differing severity. The items are feasible, grounded in theory, and reflect expert opinion as well as practice setting realities. The items have the potential for use to assess current practice in organizations and set future improvement goals.
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 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 | 0.031 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".