National consensus on the capabilities that inform the role of advanced practice paramedics: A Delphi study
Bibliographic record
Abstract
Advanced Practice Paramedics (APPs) are highly skilled paramedics who operate in diverse clinical settings both within and outside traditional ambulance services. International evidence demonstrates that APPs enhance patient outcomes in areas such as critical and primary care. In Australia, the expansion of APP roles and responsibilities has gained momentum; however, no nationally recognised framework exists to define their expected capabilities. This gap leads to inconsistencies in education, practice and role clarity. A standardised capability framework is therefore vital to guide the development, implementation and integration of APP roles within the Australian healthcare system. The primary aim of this study was to develop a comprehensive list of APP capabilities tailored to the Australian context through expert consensus. A modified Delphi approach was used across four iterative phases to establish consensus. An expert panel of clinical, academic, organisational and regulatory/governance leaders was identified via the Knowledge Resource Nomination Worksheet. Participants reviewed, rated and refined proposed APP capabilities derived from international frameworks and relevant literature. Consensus was defined as a minimum of 70% agreement among participants. Of the experts invited, 43 consented to participate in the Delphi process. A final set of 33 capabilities, achieving 96% overall consensus, was developed. These capabilities spanned four key domains: Clinical Practice (14), Leadership and Management (10), Education (7) and Research (2). Iterative feedback ensured each capability was clear, relevant and aligned with the Australian healthcare context. The resulting capability framework provides a robust foundation for standardising APP roles within Australia, promoting consistency in education, practice and professional expectations. This framework not only supports the national advancement of APPs but may also serve as a model for international adaptation, contributing to the global development and recognition of advanced practice roles 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 | 0.198 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".