The regulation of healthcare professions and support workers in international context
Bibliographic record
Abstract
BACKGROUND: The objective of this paper is to outline and compare the regulation of paid healthcare professions and associated support workers in international context, bringing out the lessons to be learned as appropriate. Modern neo-liberal societies have sought to enhance healthcare through greater professional regulation, albeit in different ways and at variable pace. This general trend is illustrated with reference to medicine in the UK. However, although such reforms have helpfully cascaded to other health professions, government policy in high-income countries has not yet adequately regulated the interrelated group of non-professionalised health support workers who form the largest and least recognised part of the workforce. Nonetheless, in low- and middle-income (LMIC) countries-aside from the greater need for regulation of health professions-there is even more of an imperative to regulate the disparate, largely invisible support workforce. METHODS: With reference to existing studies of the medical and wider health professions in the UK and selected other higher income societies, the importance of health professional regulation to the public is underlined in the Global North. The larger gap in the regulation of support workers in modern neo-liberal countries is also emphasised on a similar basis, with an increasingly ageing population and advances in healthcare. It is argued from the very limited patchwork of secondary literature, though, that policy-makers may want to focus even more on enhancing regulation of both the professional and non-professional workforce in LMIC societies centred mainly in the Global South, drawing on lessons from the Global North. RESULTS/CONCLUSIONS: Efforts to reform health professional regulatory approaches in more economically developed countries, while needing refinement, are likely to have had a positive effect. However, even in these societies there are still substantial shortfalls in the regulation of health support workers. There are even larger gaps in LMICs where there are fewer health professional staff and a greater dependence on support workers. With higher rates of morbidity and mortality, there is much more scope here for reforming health regulation in the public interest to extend standards and mitigate risk, following the pattern for healthcare professions in the Global North.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".