Characterising support and care assistants in formal hospital settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: A 15 million health workforce shortage is still experienced globally leading to a sub-optimal healthcare worker-to-population ratio in most countries. The use of low-skilled care assistants has been suggested as a cost-saving human resource for health strategy that can significantly reduce the risks of rationed, delayed, or missed care. However, the characterisation, role assignment, regulation, and clinical governance mechanisms for unlicensed assistive workforce remain unclear or inconsistent. The purpose of this study was to map and collate evidence of how care assistants are labelled, utilised, regulated, and managed in formal hospital settings as well as their impact on patient care. METHODS: We conducted a scoping review of literature from PUBMED, CINAHL, PsychINFO, EMBASE, Web of Science, Scopus, and Google Scholar. Searches and eligibility screening were conducted using the Participants-Context-Concepts framework. Thematic content analysis guided the synthesis of the findings. RESULTS: 73 records from a total of 15 countries were included in the final full-text review and synthesis. A majority (78%) of these sources were from high-income countries. Many titles are used to describe care assistants, and these vary within and across countries. On ascribed roles, care assistants perform direct patient care, housekeeping, clerical and documentation, portering, patient flow management, ordering of laboratory tests, emergency response and first aid duties. Additional extended roles that require higher competency levels exist in the United States, Australia, and Canada. There is a mixture of both positive and negative sentiments on their impact on patient care or nurses' perception and experiences. Clinical and organisational governance mechanisms vary substantially across the 15 countries. Licensure, regulatory mechanisms, and task-shifting policies are largely absent or not reported in these countries. CONCLUSIONS: The nomenclature used to describe care assistants and the tasks they perform vary substantially within countries and across healthcare systems. There is, therefore, a need to review and update the international and national classification of occupations for clarity and more meaningful nomenclature for care assistants. In addition, the association between care assistants and care outcomes or nurses' experience remains unclear. Furthermore, there is a dearth of empirical evidence on this topic from low- and middle-income countries.
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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 | 0.022 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".