Methodology for the development of a Canadian national EMS research agenda
Bibliographic record
Abstract
BACKGROUND: Many health care disciplines use evidence-based decision making to improve patient care and system performance. While the amount and quality of emergency medical services (EMS) research in Canada has increased over the past two decades, there has not been a unified national plan to enable research, ensure efficient use of research resources, guide funding decisions and build capacity in EMS research. Other countries have used research agendas to identify barriers and opportunities in EMS research and define national research priorities. The objective of this project is to develop a national EMS research agenda for Canada that will: 1) explore what barriers to EMS research currently exist, 2) identify current strengths and opportunities that may be of benefit to advancing EMS research, 3) make recommendations to overcome barriers and capitalize on opportunities, and 4) identify national EMS research priorities. METHODS: Paramedics, educators, EMS managers, medical directors, researchers and other key stakeholders from across Canada will be purposefully recruited to participate in this mixed methods study, which consists of three phases: 1) qualitative interviews with a selection of the study participants, who will be asked about their experience and opinions about the four study objectives, 2) a facilitated roundtable discussion, in which all participants will explore and discuss the study objectives, and 3) an online Delphi consensus survey, in which all participants will be asked to score the importance of each topic discovered during the interviews and roundtable as they relate to the study objectives. Results will be analyzed to determine the level of consensus achieved for each topic. DISCUSSION: A mixed methods approach will be used to address the four study objectives. We anticipate that the keys to success will be: 1) ensuring a representative sample of EMS stakeholders, 2) fostering an open and collaborative roundtable discussion, and 3) adhering to a predefined approach to measure consensus on each topic. Steps have been taken in the methodology to address each of these a priori concerns.
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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 | 0.121 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".