Lessons From the COVID-19 Pandemic to Improve the Health, Social Care, and Well-being of Minoritized Ethnic Groups With Chronic Conditions or Impairments: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has inequitably impacted the experiences of people living with ill health/impairments or from minoritized ethnic groups across all areas of life. Given possible parallels in inequities for disabled people and people from minoritized ethnic backgrounds, their existence before the pandemic and increase since, and the discriminations that each group faces, our interest is in understanding the interplay between being disabled AND being from a minoritized ethnic group. OBJECTIVE: The overarching aim of the Coronavirus Chronic Conditions and Disabilities Awareness (CICADA) project, building on this understanding, is to improve pandemic and longer-term support networks, and access to and experiences of care, services, and resources for these underserved groups, both during the pandemic and longer term, thereby reducing inequities and enhancing social, health, and well-being outcomes. METHODS: This mixed methods study involves three "sweeps" of a new UK survey; secondary analyses of existing cohort and panel surveys; a rapid scoping review; a more granular review; and qualitative insights from over 200 semistructured interviews, including social network/map/photo elicitation methods and two subsequent sets of remote participatory research workshops. Separate stakeholder cocreation meetings, running throughout the study, will develop analyses and outputs. Our longitudinal study design enables the exploration of significant relationships between variables in the survey data collected and to the assessment of changes in variables over time, including consideration of varying pandemic contexts. The qualitative data will provide more granular detail. We will take a strengths and assets-based approach, underpinned by the social model of disability and by intersectional considerations to challenge discrimination. Our exploration of the social determinants of health and well-being is framed by the social ecological model. RESULTS: The CICADA project was funded by the Health and Social Care Delivery Research (HSDR) Programme of the United Kingdom (UK) National Institute for Health and Care Research (NIHR) in March 2021 and began in May 2021. Further work within the project (84 interviews) was commissioned in March 2022, a substudy focusing on mental health, specifically in Northeast England, Greater Manchester, and the Northwest Coast of the United Kingdom. Data collection began in August 2021, with the last participants due to be recruited in September 2022. As of January 2022, 5792 survey respondents and 227 interviewees had provided data. From April 2022, the time of article submission, we will recruit participants for the substudy and wave 2 of the surveys and qualitative work. We expect results to be published by winter 2022. CONCLUSIONS: In studying the experiences of disabled people with impairments and those living with chronic conditions who come from certain minoritized ethnic groups, we are aiming for transformative research to improve their health and well-being. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38361.
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 | 0,093 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,009 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,065 | 0,015 |
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 ».