Identifying relevant topics and training methods for emergency department flow training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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 it