LO28: The Featured Leadership & Organization Workplace (FLOW) Hacks Series: Using the FOAMed domain for knowledge exchange and transfer of emergency department quality improvement projects
Bibliographic record
Abstract
Introduction/Innovation Concept: Emergency departments (ED) across Canada have experienced increased patient volumes and greater demands on resources. Quality improvement (QI) projects have become common in the ED with the goal of providing better and more efficient care. These projects typically attempt to improve resource utilization or patient experience. Unfortunately, the opportunity to share and exchange information among physicians about QI projects is limited. The Free Open Access Medical Education (FOAMed) domain provides a good opportunity for physicians to share their successes and challenges when implementing QI projects. The Featured Leadership & Organizational Workplace (FLOW) Hacks is an ongoing dissemination project hosted on CanadiEM.org that aims to provide ED physicians with a forum for knowledge exchange and transfer. Methods: Emergency physician leaders from across Canada have been recruited to share their QI experiences. The FLOW Hacks are summarized as a standardized set of questions that aim to convey the most important aspects of the QI project. The physician responses are published on a monthly basis as a feature on the site. Our objective is to represent EDs from across Canada and of variable size. Curriculum, Tool, or Material: Our standardized questions collect information not only on the innovation and team members but also the methodology used for the QI initiative, the data collected, and the performance measures used to assess the outcome. There is a particular focus placed on the challenges that were encountered in implementing the initiative, how they were overcome, and how they would change their approach if they could redo the project. The goal of this format is to showcase the best QI initiatives in Canada so that others can replicate the work and learn from the challenges and success of the authors. Conclusion: The FLOW Hacks series is an innovative project to disseminate QI projects to emergency physicians and managers. In the next phase of this project we will conduct a qualitative analysis of the published FLOW Hacks to identify the common mistakes and best practices in implementation of QI initiatives.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".