High stakes and high emotions: providing safe care in Canadian emergency departments
Bibliographic record
Abstract
BACKGROUND: The high-paced, unpredictable environment of the emergency department (ED) contributes to errors in patient safety. The ED setting becomes even more challenging when dealing with critically ill patients, particularly with children, where variations in size, weight, and form present practical difficulties in many aspects of care. In this commentary, we will explore the impact of the health care providers' emotional reactions while caring for critically ill patients, and how this can be interpreted and addressed as a patient safety issue. DISCUSSION: ED health care providers encounter high-stakes, high-stress clinical scenarios, such as pediatric cardiac arrest or resuscitation. This health care providers' stress, and at times, distress, and its potential contribution to medical error, is underrepresented in the current medical literature. Most patient safety research is limited to error reporting systems, especially medication-related ones, an approach that ignores the effects of health care provider stress as a source of error, and limits our ability to learn from the event. Ways to mitigate this stress and avoid this type of patient safety concern might include simulation training for rare, high-acuity events, use of pre-determined clinical order sets, and post-event debriefing. CONCLUSION: While there are physiologic and anatomic differences that contribute to patient safety, we believe that they are insufficient to explain the need to address critical life-threatening event-related patient safety issues for both adults and, especially, children. Many factors make patient safety during critical medical events distinct from general patient safety issues, but it is, perhaps, this heightened high-stress, emotional climate that is the most distinct and important part of all. We believe that consideration of this concept is essential when discussing safety improvement in critical medical events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".