Paramedic clinical decision making during high acuity emergency calls: design and methodology of a Delphi study
Bibliographic record
Abstract
BACKGROUND: The scope of practice of paramedics in Canada has steadily evolved to include increasingly complex interventions in the prehospital setting, which likely have repercussions on clinical outcome and patient safety. Clinical decision making has been evaluated in several health professions, but there is a paucity of work in this area on paramedics. This study will utilize the Delphi technique to establish consensus on the most important instances of paramedic clinical decision making during high acuity emergency calls, as they relate to clinical outcome and patient safety. METHODS AND DESIGN: Participants in this multi-round survey study will be paramedic leaders and emergency medical services medical directors/physicians from across Canada. In the first round, participants will identify instances of clinical decision making they feel are important for patient outcome and safety. On the second round, the panel will rank each instance of clinical decision making in terms of its importance. On the third and potentially fourth round, participants will have the opportunity to revise the ranking they assigned to each instance of clinical decision making. Consensus will be considered achieved for the most important instances if 80% of the panel ranks it as important or extremely important. The most important instances of clinical decision making will be plotted on a process analysis map. DISCUSSION: The process analysis map that results from this Delphi study will enable the gaps in research, knowledge and practice to be identified.
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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 | 0.107 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".