Barriers to paramedic professionalisation: a qualitative enquiry across the UK, Canada, Australia, USA and the republic of Ireland
Bibliographic record
Abstract
BACKGROUND: Paramedicine is undergoing a transformative shift as practitioners seek recognition beyond traditional emergency response roles toward being fully integrated healthcare professionals. Central to this evolution is the process of professionalisation, marked by efforts to expand scope of practice, formalise education and regulation, and achieve greater systemic integration. Despite these developments, significant barriers remain. PURPOSE: This study explores key barriers to the professionalisation of paramedics across five developed healthcare systems, highlighting shared and context-specific challenges. METHODS: A qualitative study underpinned by a critical theory paradigm was conducted using semi-structured interviews. Over a five-month period (Dec 2022-Apr 2023), 15 expert stakeholders from clinical, educational, policy, and leadership roles in paramedicine and pre-hospital emergency care were recruited across five countries. Interviews were conducted via Microsoft Teams, transcribed verbatim, and analysed thematically with a reflexive and interpretive approach. RESULTS: Four main themes were developed: Current Barriers to Expansion- including outdated legislation, inconsistent regulatory frameworks, limited funding, workforce shortages, and insufficient integration within healthcare systems. Elevating Professional Status- focusing on the need for protected titles, standardised education, credentialing, and a stronger professional identity. Impact of COVID-19- participants reflected on the profession's temporary visibility during the pandemic, followed by policy and funding shifts that diluted that momentum. Future Continuing and Emerging Barriers- encompassing structural and cultural resistance, lack of leadership pathways, and challenges in sustaining innovation and collaboration. CONCLUSION: The study highlights persistent barriers to paramedic professionalisation, including fragmented regulation, uneven educational standards, and systemic underinvestment. Although COVID-19 demonstrated the adaptability and potential of the profession, sustaining progress requires targeted policy reform, stronger regulatory frameworks, investment in education and leadership, and commitment to workforce development. Recognising paramedics as integral healthcare providers is essential to advancing the profession and improving patient care.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".