Barriers and facilitators to the implementation of a national research strategy for paramedicine in Ireland: Findings from a focus group study
Bibliographic record
Abstract
Background Paramedicine research and education has progressed significantly over the past two decades in Ireland. The Pre-Hospital Emergency Care Council recently commissioned a research team to develop a new National Research Strategy for Irish paramedicine. The strategy will serve to build research capacity and establish research priorities into the future. This study aims to explore the perspectives of diverse stakeholders to identify barriers and facilitators to the implementation of a national research strategy for paramedicine in Ireland. Methods The research employed an action research approach. Purposeful and snowball sampling was used to identify and recruit participants (n=37) from diverse stakeholders and knowledge users working in the out-of-hospital setting. Data collection took the form of focus groups (n=6) facilitated by experienced researchers. Audio recordings were transcribed verbatim using Otter AI and thematic analysis was performed in NVivo. Results Barriers and facilitators to the implementation of the research strategy exist at individual and system levels. The themes developed from the Focus Groups included; Challenges in the Research Landscape and Opportunities to Build a Research Ecosystem. Individual barriers included Experience and Skills, Time and Wellbeing. At system level, Operational, Educational, and Professional barriers were identified in addition to an undeveloped Research Infrastructure (Leadership, Support, Funding and Data Access). Individual facilitators included; Professional Identity, Evolving Roles and Protected Time. At system level, Collaboration, Knowledge Translation, Educational facilitators and Professional facilitators, in combination with a strong Research Infrastructure were considered important for successful implementation of the Research Strategy. Conclusions The findings are congruent with international studies and highlight the complexity of implementing a national research strategy aligned with the needs of diverse stakeholders in the out-of-hospital setting. Collaboration will be essential for successful strategy implementation. To ensure optimal effectiveness of the research strategy the development of an implementation plan is recommended.
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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 | 0.082 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".