Mental health in the pandemic: a repeated cross-sectional mixed-method study protocol to investigate the mental health impacts of the coronavirus pandemic in the UK
Bibliographic record
Abstract
INTRODUCTION: The WHO declared a global pandemic on 11 March 2020. Since then, the world has been firmly in the grip of the COVID-19. To date, more than 211 730 035 million confirmed cases and more than 4 430 697 million people have died. While controlling the virus and implementing vaccines are the main priorities, the population mental health impacts of the pandemic are expected to be longer term and are less obvious than the physical health ones. Lockdown restrictions, physical distancing, social isolation, as well as the loss of a loved one, working in a frontline capacity and loss of economic security may have negative effects on and increase the mental health challenges in populations around the world. There is a major demand for long-term research examining the mental health experiences and needs of people in order to design adequate policies and interventions for sustained action to respond to individual and population mental health needs both during and after the pandemic. METHODS AND ANALYSIS: This repeated cross-sectional mixed-method study conducts regular self-administered representative surveys, and targeted focus groups and semi-structured interviews with adults in the UK, as well as validation of gathered evidence through citizens' juries for contextualisation (for the UK as a whole and for its four devolved nations) to ensure that emerging mental health problems are identified early on and are properly understood, and that appropriate policies and interventions are developed and implemented across the UK and within devolved contexts. STATA and NVIVO will be used to carry out quantitative and qualitative analysis, respectively. ETHICS AND DISSEMINATION: Ethics approval for this study has been granted by the Cambridge Psychology Research Ethics Committee of the University of Cambridge, UK (PRE 2020.050) and by the Health and Life Sciences Research Ethics Committee of De Montfort University, UK (REF 422991). While unlikely, participants completing the self-administered surveys or participating in the virtual focus groups, semi-structured interviews and citizens' juries might experience distress triggered by questions or conversations. However, appropriate mitigating measures have been adopted and signposting to services and helplines will be available at all times. Furthermore, a dedicated member of staff will also be at hand to debrief following participation in the research and personalised thank-you notes will be sent to everyone taking part in the qualitative research.Study findings will be disseminated in scientific journals, at research conferences, local research symposia and seminars. Evidence-based open access briefings, articles and reports will be available on our study website for everyone to access. Rapid policy briefings targeting issues emerging from the data will also be disseminated to inform policy and practice. These briefings will position the findings within UK public policy and devolved nations policy and socioeconomic contexts in order to develop specific, timely policy recommendations. Additional dissemination will be done through traditional and social media. Our data will be contextualised in view of existing policies, and changes over time as-and-when policies change.
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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 | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".