Learning from preventable adverse events in health care organizations
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.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".