Continuing education meets the learning organization: The challenge of a systems approach to patient safety
Bibliographic record
Abstract
Since the release of the report of the Institute of Medicine on medical errors and patient safety in November 1999, health policy makers and health care leaders in several nations have sought solutions that will improve the safety of health care. This attention to patient safety has highlighted the importance of a learning approach and a systems approach to quality measurement and improvement. Balanced with the need for public disclosure of performance, confidential reporting with feedback is one of the prime ways that nations such as the United States, Canada, the United Kingdom, and Australia have approached this challenge. In the United States, the Quality Interagency Coordination Task Force has convened federal agencies that are involved in health care quality improvement for a coordinated initiative. Based on an investment in a strong research foundation in health care quality measurement and improvement, there are eight key lessons for continuing education if it is to parlay the interest in patient safety into enhanced continuing education and quality improvement in learning health care systems. The themes for these lessons are (1) informatics for information, (2) guidelines as learning tools, (3) learning from opinion leaders, (4) learning from the patient, (5) decision support systems, (6) the team learning together, (7) learning organizations, and (8) just-in-time and point-of care delivery.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".