Extended Roles of Non‐physician Health Professionals and Innovative Models of Care within Europe: Results from a Web‐based Survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Due to the increasing prevalence of rheumatic diseases, extended roles of non-physician health professionals and innovative models of care may be important options in rheumatology in the future. Extended roles have been pioneered in the UK, Canada, USA and Australia and been found to be effective and safe. However, few data are available about mainland Europe, so the aim of this study was to explore the current status of the extended roles undertaken by health professionals within Europe, and the corresponding models of care used. METHODS: Non-physician health professionals from various European countries were asked to complete a web-based survey using convenience and snowball sampling techniques. Data analysis involved calculating descriptive statistics and frequencies based on the countries where the participants currently worked. RESULTS: Of the 479 health professionals who filled in the survey, 430 (92%) indicated that they were performing extended roles. Considerable differences between the 27 participating countries existed, in terms of which extended roles and which innovative models of care were being used. Barriers to performing extended roles were cited as the attitude of rheumatologists in all but eight countries, while attitudes of patients were less common barriers. Lack of knowledge, education and educational opportunities were also experienced in several countries. CONCLUSION: The present study produced the first data on extended roles for non-physician health professionals and corresponding innovative models of care in rheumatology within Europe. We recommend increasing educational opportunities, as well as developing strategies to limit the barriers experienced.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".