Identifying relevant topics and training methods for emergency department flow training
Bibliographic record
Abstract
PURPOSE: Despite the importance of patient flow to emergency department (ED) management, there is a need to strengthen and expand training in flow strategies for practicing ED staff. To date, there has been limited academic inquiry into the skills and training that ED staff require to improve patient flow. As part of a quality improvement initiative, our team aimed to identify the topics and training methods that should be included in flow training for ED staff. METHODS: We conducted an integrative review and modified Delphi. For the integrative review, we sought to identify appropriate skills, training strategies, and training modalities to include in a curriculum for ED staff. The findings from the review were compiled and distributed to Canadian experts in ED efficiency through a modified Delphi, including physicians, nurses, and nurse practitioners. RESULTS: Our literature search retrieved 8359 articles, of which 46 were included in the review. We identified 19 skills, 9 training strategies, and 12 training modalities used to improve ED efficiency in the literature. For the modified Delphi, we received responses from 39 participants in round one and 28 in round two, with response rates of 57% and 41%, respectively. The topics chosen by the most respondents were: "flow decisions," "teamwork," "backlog and surge management," "leadership," and "situational awareness." CONCLUSION: Our findings suggest that flow training should teach ED staff how to make decisions that improve flow, work more effectively as a team, manage patient backlog and surge, improve leadership skills, and develop situational awareness. These findings add to a gap in the academic literature regarding the training ED staff require to improve patient flow.
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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 | 0.060 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.030 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".