Creative Research Approaches For Complex Questions In Pre-Hospital Emergency Medicine
Bibliographic record
Abstract
Pre-Hospital Emergency Medical Service work environments are complex, presenting unique challenges for the design of environments and equipment, digital technologies, and inter-organizational operational strategies. As researchers, our challenge is to understand how providers operate in these complex environments, especially during emergency situations, to ensure solutions to these challenges are well informed by representative evidence. This requires collecting data regarding human-system interactions occurring between many people with shifting roles, in dynamic and potentially dangerous environments, with complex patients and often acute time constraints. A lot is happening simultaneously, and our ability to learn and provide meaningful insights is challenged. In search for methods that will help us asses these situations we often use mixed methods that are adapted to the unique and changing conditions of each study. The goal of this session was to present Case Studies and methods used for the panelist’s research involving the emergency medical services work environment. We took a close look at the tools and methods employed by the panelists for their research, and learned about the benefits and limitations of their unique approaches, as they were implemented in unique contexts.
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 | 0.091 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".