Bibliographic record
Abstract
Long-term care is an increasingly important sector of employment for migrant workers, both in the United Kingdom (UK) and in most immigrant-receiving countries. This study investigated the current and potential future demand for migrant carers in ageing societies; the experiences of migrant care workers, of their employers and of older people in residential and home care settings; and the implications of these findings for the social care and for migration policies. Focusing on the UK, it was one of four country studies conducted between 2007 and 2009 in the UK, Ireland, the USA and Canada. The research methodology included analysis of existing national data sources on the social care workforce, a postal and online survey of care organisations, collection of qualitative data from migrant care workers and older care users and projections of future demand for migrant workforce in older adult care. The dataset is drawn from a survey of organisations providing care services to older people in the UK carried out between January and June 2008. Surveyed employers were asked to regard as 'migrants' people who were born abroad. The questionnaire mainly included closed-end questions and focused on the structure of the workforce, the reasons for the reliance on migrant workers, the recruitment process, the management implications of the employment of migrant staff, and employer experiences with immigration regulations. Further information about the study is available from the COMPAS Migrant Care Workers in Ageing Societies UK web page.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".