Inequalities and mental health during the Coronavirus pandemic in the UK: a mixed-methods exploration
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,043 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».