No Two Systems Are the Same: Paramedic Perceptions of Contemporary System Performance Using Prehospital Quality Indicators
Bibliographic record
Abstract
Introduction In recent years, researchers have identified two new models of paramedicine within the Anglo-American paramedic system known as the Directive and Professionally Autonomous paramedic systems. The research team now seek to compare paramedic perception of system performance between the two systems using prehospital quality indicators. Methods Paramedics employed within Anglo-American paramedic systems undertook a survey evaluating their experience and perception of system performance against a set of modified prehospital quality indicators. Data were collected using a survey combining single-choice questions with matrix multiple-choice questions. Key results were cross-tabulated with demographic (informant) and system factors to compare performance between the two new paramedic systems. Results The survey indicated a substantial difference in perceived clinical and operational performance between the Professionally Autonomous and Directive paramedic systems, with the Professionally Autonomous paramedic system performing consistently better in all 11 prehospital quality indicator domains. Conclusion The results of this survey are a vital step in helping paramedics, health leaders, and academics understand the complex relationship between paramedic system design and system performance, and, for the first time, provides empirical evidence upon which to make a conscious decision to adopt one system or the other.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".