Exploring Paramedicine's Research Infrastructure in Ontario, Canada
Bibliographic record
Abstract
Objective: Paramedicine is expanding in scope and diversifying its contributions to healthcare systems and society. To achieve sustainable and meaningful development, paramedicine (like other health professions) must prioritize the generation and use of high-quality evidence to guide practice, policy, and innovation. However, paramedicine's progression has been criticized for lacking a sufficient evidence base, undermining its decision-making. We sought to examine the existing infrastructure within a paramedicine context that supports the community to engage in, produce, and/or use research. Methods: This qualitative study employed semistructured interviews analyzed using reflexive thematic analysis. Purposive and snowball sampling was used to recruit and enroll those engaged in research capacity development and contributions in Ontario, Canada, representing diverse roles within the paramedicine community. The interview guide was informed by Cooke's six-principle research capacity framework: skills, collaboration, infrastructure, ownership, research-practice linkages, and culture. Data were transcribed, coded, and thematically analyzed using Braun and Clarke's six-phase method. Member checking was employed, offering participants the opportunity to review their transcripts for accuracy and to provide revision/elaborations. Results: Twenty-four individuals were interviewed. Two overarching themes emerged: (1) Structural and Cultural Foundations for Research, highlighting infrastructure challenges, fragmented pathways, cultural resistance, and a reliance on informal networks, and (2) Systemic Integration and Strategic Alignment underscoring calls for long-term growth, broader healthcare system integration, solving inequities in access to research infrastructure, better data access and governance, clear leadership on research, and demonstrations of value. Conclusion: Ontario's paramedicine community faces significant challenges to research engagement, capacity building, and contributions, hindering the profession's ability to support the growth and use of a research and evidence ecosystem. Several implications and recommendations are outlined, including attending to internal (e.g., research infrastructure, professional integration) and external (e.g., strengthening collaboration and partnerships) factors and policy goals. International contexts are considered.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".