The Structure and Characteristics of Anglo-American Paramedic Systems in Developed Countries: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction Paramedicine has undergone significant change in the past two decades. While the Anglo-American paramedic system continues to grow there appears to be a dearth of published literature regarding modern categorisation of this evolving paramedic system. The objective of this scoping review is to examine and map the existing evidence to provide an overview of the characteristics and structural similarities and differences of Anglo-American paramedic systems in English-speaking developed countries. Methods Databases, including Embase, MEDLINE, Web of Science, EBSCOhost, CINAHL, Google Scholar and Epistemonikos, will be searched from inception. A grey literature search strategy has also been developed to identify non-indexed relevant literature. Citations and references of included studies will also be searched. Two reviewers will undertake title and abstract screening, followed by full text screening. Data extraction will be conducted using a customised instrument. Inclusion criteria: results examining management, leadership or governance in paramedicine related to the Anglo-American paramedic model in English-speaking developed countries will be included in the review. Included studies will be summarised using narrative synthesis structured around themes of management, leadership and governance in paramedicine.
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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 | 0.084 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.029 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".