A nurse by any other name? An international comparison of nomenclature and regulation of healthcare assistants
Bibliographic record
Abstract
Background: Across international healthcare systems, healthcare assistant roles have proliferated, in part to decrease nursing costs and support workplace staffing. There is a lack of consensus about the professional title for healthcare assistants, and whether this group requires professional regulation. The variety of terms for healthcare assistants has resulted in confusion around their scope of practice and role within the healthcare team, which may influence patient care. Aim: We aimed to identify the terminology used for healthcare assistants across English speaking countries and determine the international status of professional regulation of healthcare assistants. Method: We conducted a deductive, structured search for healthcare assistant roles that were codified on English-language nursing regulator websites in each jurisdiction in Australia, New Zealand, USA, Canada, Ireland, and the United Kingdom. We assessed what terminology were used for healthcare assistant roles in each area, and whether they were regulated by a professional regulator, such as a college of nursing. Results: Across 77 jurisdictions, we identified 37 different terms for healthcare assistants. The most frequent term was Certified Nurse Aid with 24 uses, and Certified Nursing Assistant with 13 uses. The majority of healthcare assistants are not professionally regulated. Only 12 jurisdictions have professional regulation programs for healthcare assistants, all in the USA. Conclusion: There is an urgent need for international consensus about the nomenclature for healthcare assistants, so the healthcare assistant workforce can be supported, and their work evaluated via research studies. Regulators can consider how to engage with healthcare assistants and protect the public, as healthcare assistants provide an increasing proportion of 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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".