The Alberta Health Services Emergency Strategic Clinical Network™ Quality Improvement and Innovation Forum 2023
Bibliographic record
Abstract
Evidence-based research and quality improvement work are pivotal to health systems meeting their goals. Translating findings and disseminating innovative practices to new settings occurs in part through knowledge translation events, such as conferences and workshops. The annual Emergency Strategic Clinical Network™ (ESCN) Quality Improvement and Innovation Forum fills a gap between local and national events. It is devoted to sharing methods and results of emergency department projects in Alberta among those working in emergency care. The event provides an opportunity for those pursuing quality improvement initiatives in emergency medicine to network with one another, share innovative projects, share experience, and translate promising works to new settings. In addition, the event provides an opportunity to identify projects for potential development through local, provincial, or national funding opportunities. After providing only an online forum throughout the pandemic, this year’s event was held in-person in Red Deer, Alberta and live-streamed for those who wished to attend virtually. Nineteen oral presentations were delivered including a keynote address by Dr. Leigh Chapman, Chief Nursing Officer of Canada. Strong attendance shows the value practitioners see in the forum. In 2023, approximately 160 educators, managers, nurses, physicians, researchers, and patient advisors attended the forum. Post-event evaluation survey feedback suggests that most of the participants found the presentations helpful and that the virtual presentations were as effective as in-person presentations. Attendees indicated that many of the initiatives presented would be explored further. We are grateful to the Canadian Journal of Emergency Nursing for publishing abstracts from the event.
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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".