An Inter-Agency Expert Panel's Prioritisation of Clinical Quality Improvement Topics in a Paramedic System
Bibliographic record
Abstract
Introduction Quality improvement (QI) programs have become common in paramedic systems, but they are often limited to individual agencies. Modern paramedicine involves many different agencies and inter-agency QI programs would better reflect their co-operative efforts. Similarly, inter-agency use of clinical outcome measurements can offer system level performance data. This study's intent was to explore the feasibility of planning an inter-agency QI program that uses outcome measures. Methods This study used a modified Delphi methodology. A 49-member panel of inter-agency representatives was convened to identify and prioritise clinical outcome-based topics. Over a 3-month period, two online surveys were conducted followed by a 1-day face-to-face meeting. Results The study demonstrated very high participation rates. Results progressed from an initial wide range of 38 topics to a final consensus of two: infection/sepsis and patient safety/care pathways, complete with outcome measures. Conclusion Inter-agency quality improvement planning is an under investigated area, but this study demonstrates that it is feasible. Additionally, this planning can incorporate clinical outcome measures that inform system level discussions about quality. Other paramedic agencies may draw on the study's processes when planning their own quality improvement programs.
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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 | 0.071 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".