The mental health experiences of ethnic minorities in the UK during the Coronavirus pandemic: A qualitative exploration
Bibliographic record
Abstract
Background Worldwide, the Coronavirus pandemic has had a major impact on people's health, lives, and livelihoods. However, this impact has not been felt equally across various population groups. People from ethnic minority backgrounds in the UK have been more adversely affected by the pandemic, especially in terms of their physical health. Their mental health, on the other hand, has received less attention. This study aimed to explore the mental health experiences of UK adults from ethnic minorities during the Coronavirus pandemic. This work forms part of our wider long-term UK population study “Mental Health in the Pandemic.” Methods We conducted an exploratory qualitative study with people from ethnic minority communities across the UK. A series of in-depth interviews were conducted with 15 women, 14 men and 1 non-binary person from ethnic minority backgrounds, aged between 18 and 65 years old (mean age = 40). We utilized purposefully selected maximum variation sampling in order to capture as wide a variety of views, perceptions and experiences as possible. Inclusion criteria: adults (18+) from ethnic minorities across the UK; able to provide full consent to participate; able to participate in a video- or phone-call interview. All interviews took placeviaMS Teams or Zoom. The gathered data were transcribed verbatim and underwent thematic analysis following Braun and Clarke carried out using NVivo 12 software. Results The qualitative data analysis yielded seven overarching themes: (1) pandemic-specific mental health and wellbeing experiences; (2) issues relating to the media; (3) coping mechanisms; (4) worries around and attitudes toward vaccination; (5) suggestions for support in moving forward; (6) best and worst experiences during pandemic and lockdowns; (7) biggest areas of change in personal life. Generally, participants' mental health experiences varied with some not being affected by the pandemic in a way related to their ethnicity, some sharing positive experiences and coping strategies (exercising more, spending more time with family, community cohesion), and some expressing negative experiences (eating or drinking more, feeling more isolated, or even racism and abuse, especially toward Asian communities). Concerns were raised around trust issues in relation to the media, the inadequate representation of ethnic minorities, and the spread of fake news especially on social media. Attitudes toward vaccinations varied too, with some people more willing to have the vaccine than others. Conclusion This study's findings highlight the diversity in the pandemic mental health experiences of ethnic minorities in the UK and has implications for policy, practice and further research. To enable moving forward beyond the pandemic, our study surfaced the need for culturally appropriate mental health support, financial support (as a key mental health determinant), accurate media representation, and clear communication messaging from the Governments of the UK.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".