Principles to Guide the Future of Paramedicine in Canada
Bibliographic record
Abstract
Objective: Paramedicine in Canada has experienced significant growth in recent years, which has resulted in a misalignment between existing guiding conceptualizations and how the profession is structured and enacted in practice. As a result, well-established boundaries, directions, and priorities may be poorly aligned with existing frameworks. The objective of this study was to explore emerging and future states of paramedicine in Canada such that guiding principles could be derived. We asked: How should paramedicine be conceptualized and enacted in Canada going forward, and, what might be the necessary enablers? Methods: This study involved in-depth one-on-one semi-structured interviews with Canadian paramedicine thought leaders. We used purposive and snowball sampling strategies to identify potential participants. Interview guide questions were used to stimulate discussion about the future of paramedicine in Canada and suggestions for implementation. We used inductive qualitative content analysis as our analytical approach, informed by a constructivist and interpretivist orientation. Results: Thirty-five key informants from across Canada participated in interviews. Ten themes were identified: (1) prioritizing patients and their communities; (2) providing health care along a health and social continuum; (3) practicing within an integrated health care framework, and partnering across sectors; (4) being socially responsive; (5) enacting professional autonomy; (6) integrating the health of professionals; (7) using quality-based frameworks; (8) enacting intelligent access to and distribution of services; (9) enacting a continuous learning environment; and, (10) being evidence-informed in practice and systems. Six enablers were also identified: shift professional culture and identity, enhance knowledge, promote shared understanding of paramedicine, integrate data environments, leverage advancing technology, advance policy, regulation and legislation. Conclusions: Our results provide a conceptual framework made up of guiding principles and enablers that provide a consolidated lens to advance the paramedicine profession in Canada (and elsewhere as appropriate) while ensuring contextual and regional needs and differences can be accounted for.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".