Learning from preventable adverse events in health care organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Preventable adverse events represent learning opportunities. Indeed, understanding and learning from preventable adverse events are the new organizational imperatives in health care. However, health services researchers note that there is a dearth of research on learning from failure in health care and, in industry, a limited capacity to learn from incidents and failure. PURPOSE: We address the gap between awareness of preventable adverse events and knowledge that relates to how to respond to them effectively. We develop a multilevel model of learning and theorize factors that influence learning from preventable adverse events. METHODOLOGY: Drawing upon theories of organizational learning and organizational behavior, we develop a multilevel model of learning from failure, where perceived characteristics of the events, group composition and dynamics, and the behavioral and structural arrangements of health care organizations are proposed to play important roles. PRACTICAL IMPLICATIONS: Our model highlights factors that facilitate learning from failure and others that impede it. Awareness and attention to these factors can help health care managers extract learning from failures, like preventable adverse events, and may ultimately contribute to reducing the occurrence of preventable adverse events and improving quality of care.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it