Impact of COVID-19 on Ethnically Minoritised Carers in UK’s Care Home Settings: a Systematic Scoping Review
Bibliographic record
Abstract
COVID-19 has impacted disproportionately two groups in the UK: healthcare workers and people from ethnically minoritised groups. However, there is a lack of evidence on how COVID-19 affected ethnically minoritised carers in care homes. Therefore, the present study aimed to explore the available evidence regarding the impact of COVID-19 on ethnically minoritised carers in UK. The relevant records were systematically searched in Cochrane COVID-19 Study Register and WHO COVID-19 global literature. A total of 3164 records were retrieved. Following duplicate elimination and abstract, title, and full-text screening, 10 studies were identified as eligible for the present scoping review. Most of the studies were conducted in the UK and USA, involving diverse healthcare occupations and methodologies. Multiple studies found anxiety, depression, stress, and post-traumatic stress disorder among carers with high odds among ethnically minoritised carers. Limited access to personal protective equipment and workplace discrimination was noted and linked with poor mental health. The carers reported difficulties in care delivery and managing extra workload arising from staff shortages. The risk of infection and clinically significant mental disorders was higher among carers from the ethnically minoritised background. They exhibited fear about care homes' uncertain futures and consequential financial losses. Conclusively, COVID-19 appeared to exert adverse effects on practices and experiences of ethnically minoritised carers in the UK's care homes; however, further studies are warranted to increase the understanding of COVID-19-related experiences of this group of carers which significantly contribute to the country's healthcare system.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".