Bibliographic record
Abstract
The emergency department (ED) is a complex environment. Its equilibrium, or homeostasis, is critically dependent on the continuous action of feedback processes. For any system to function efficiently, it needs to know the outcomes of specific actions in a consistent, reliable, and expeditious way. Historical attitudes and the unique operating characteristics of the ED have combined to impose sanctions on the proper provision of feedback. The following features have been identified as obstructive to optimal feedback operation: incomplete awareness of the significance of the problem, excessive time and work pressures, case infrequency, deficiencies in specialty follow-up, communication failures, deficient reporting systems for near-misses, error, and adverse events, biases in case review processes, shift changeover times, and shiftwork. The result is that clinicians, nurses, and trainees are working in conditions that are suboptimal for the provision of safe care, as well as for learning and job fulfillment. Good feedback is a necessary condition for well-calibrated performance by individuals, and is integral to effective team function. More needs to be known about outcomes for feedback to work efficiently. The critical role of feedback in other aspects of ED function, such as education and human factors engineering, should be emphasized. The current interest in medical error and evolving attitudes toward a new culture of patient safety provide a unique opportunity to examine feedback and the critical role it plays in ED function.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".