The international Paramedic PhD registry: an overview of paramedic doctorates
Bibliographic record
Abstract
Introduction: The paramedic profession is developing at speed internationally. To stabilise this growth, the profession must produce, maintain and enhance its own evidence base. This requires doctoral-level training and development of paramedics to join and lead multidisciplinary research teams in order to produce robust science. The aim of this article is to provide an overview of paramedics across the globe who have completed, or are studying for, a doctoral-level qualification in the field of paramedicine. Methods: The Paramedic PhD registry - an English-language open-access voluntary submission platform established in November 2017 - was used as the primary source of data to inform this article. The registry relied on voluntary submissions of doctoral-level qualification details from the field of paramedicine, including doctorate title, details, institution, primary supervisor and date range. The registry was publicly available and received no funding or sponsorship. Paramedics, along with other clinical and non-clinical researchers, were able to submit their doctorate details to the registry at any time. Results: From its inception to February 2025, 305 doctorates have been registered on Paramedic PhD. Of these, 268 were from self-identified paramedics. These paramedics were spread across the United Kingdom (n = 80), Australia (n = 75), the United States (n = 27), Saudi Arabia (n = 21), Canada (n = 17) and South Africa (n = 13), along with 18 other countries. The earliest doctoral qualification by a paramedic was completed in 2002. Paramedics undertook the Doctor of Philosophy (n = 224), the Professional Doctorate (n = 32) and the Doctor of Education (n = 12) routes, with 129 registered as complete. The most popular categories were education (n = 55), professional development (n = 39) and cardiac arrest (n = 21). Conclusion: As more paramedics focus their careers on research and the pursuit of doctoral-level qualifications, the volume and quality of evidence will continue to rise, improving outcomes for patients and staff across the globe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".