Inequalities and mental health during the Coronavirus pandemic in the UK: a mixed-methods exploration
Bibliographic record
Abstract
BACKGROUND: The World Health Organisation declared the novel Coronavirus disease (COVID-19) a global pandemic on 11th March 2020. Since then, the world has been firmly in its grip. At the time of writing, there were more than 767,972,961 million confirmed cases and over 6,950,655 million deaths. While the main policy focus has been on controlling the virus and ensuring vaccine roll-out and uptake, the population mental health impacts of the pandemic are expected to be long-term, with certain population groups affected more than others. METHODS: The overall objectives of our 'Coronavirus: Mental Health and the Pandemic' study were to explore UK adults' experiences of the Coronavirus pandemic and to gain insights into the mental health impacts, population-level changes over time, current and future mental health needs, and how these can best be addressed. The wider mixed-methods study consisted of repeated cross-sectional surveys and embedded qualitative sub-studies including in-depth interviews and focus group discussions with the wider UK adult population. For this particular inequalities and mental health sub-study, we used mixed methods data from our cross-sectional surveys and we carried out three Focus Group Discussions with a maximum variation sample from across the UK adult population. The discussions covered the broader topic of 'Inequalities and mental health during the Coronavirus pandemic in the UK' and took place online between April and August 2020. Focus Groups transcripts were analysed using thematic analysis in NVIVO. Cross-sectional survey data were analysed using STATA for descriptive statistics. RESULTS: Three broad main themes emerged, each supporting a number of sub-themes: (1) Impacts of the pandemic; (2) Moving forward: needs and recommendations; (3) Coping mechanisms and resilience. Findings showed that participants described their experiences of the pandemic in relation to its impact on themselves and on different groups of people. Their experiences illustrated how the pandemic and subsequent measures had exacerbated existing inequalities and created new ones, and triggered various emotional responses. Participants also described their coping strategies and what worked and did not work for them, as well as support needs and recommendations for moving forward through, and out of, the pandemic; all of which are valuable learnings to be considered in policy making for improving mental health and for ensuring future preparedness. CONCLUSIONS: The pandemic is taking a long-term toll on the nations' mental health which will continue to have impacts for years to come. It is therefore crucial to learn the vital lessons learned from this pandemic. Specific as well as whole-government policies need to respond to this, address inequalities and the different needs across the life-course and across society, and take a holistic approach to mental health improvement across 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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".