2018 <i>Academic Emergency Medicine</i> Consensus Conference: A Workforce Development Research Agenda for Pediatric Care in the Emergency Department
Bibliographic record
Abstract
Each year, more than 30 million children visit U.S. emergency departments (EDs). Although the number of pediatric emergency medicine specialists continues to rise, the vast majority of children are cared for in general EDs outside of children's hospitals. The diverse workforce of care providers for children must possess the knowledge, experience, skills, and systemic support necessary to deliver excellent pediatric emergency care. There is a crucial need to understand the factors that drive the professional development and support systems of this diverse workforce. Through the iterative process culminating with the 2018 Academic Emergency Medicine consensus conference, we have identified five key research themes and prioritized a specific research agenda. These themes represent critical gaps in our understanding of the development and maintenance of the pediatric emergency care workforce and allow for a prioritization of future research efforts. Only by more fully understanding the gaps in workforce needs, and the necessary steps to address these gaps, can outcomes be optimized for children in need of emergency care.
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 | 0.109 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".