An Accessible Web-Based Survey to Monitor the Mental Health of People With Mild Intellectual Disability or Low Literacy Skills During the COVID-19 Pandemic: Comparative Data Analysis
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic and related control measures affected the mental health of all populations. Particular subgroups are underrepresented in mainstream surveys because they are hard to reach, and study measurements are not adapted to their skills. These subgroups include people with lower cognitive and literacy skills, such as people with mild intellectual disability (MID), who were considered vulnerable during the COVID-19 pandemic given their low socioeconomic status, small social networks, increased risks of health problems, and difficulties understanding health-related information. OBJECTIVE: This study examines the impact of the COVID-19 pandemic on mental health among people with MID or low literacy skills compared with those predominantly represented in national surveys. METHODS: A repeated cross-sectional study of people with MID or low literacy skills and a general population sample was conducted in the Netherlands. An easy-read web-based survey was co-designed with, and tested among, people with MID or low literacy skills and conducted in 3 rounds within 1 year of the COVID-19 pandemic (T1: November to December 2020, T2: March to April 2021, and T3: September to October 2021). The survey contained questions about demographics and 6 aspects of mental health: feeling happy, feeling energized, feeling stressed, worry, feeling lonely, and sleeping problems. RESULTS: Our adapted survey and recruitment procedure enabled 1059 persons with MID or low literacy skills to participate (T1: n=412, 38.9%; T2: n=351, 33.1%; and T3: n=296, 28%). They were significantly younger, had a lower level of education, and more often than not were born outside the Netherlands compared to the general population sample (P<.001). Approximately half of them (604/1059, 57.03%) received professional care. They displayed poorer mental health scores than the general population sample. The percentages of people with MID or low literacy skills who reported more negative feelings in T1 ranged from 20.6% (85/412) reporting feeling lonely often or almost always to 57.8% (238/412) reporting feeling happy almost never or sometimes. The general population sample's percentages were 5.4% (160/2930) and 32.2% (941/2918), respectively. Although scores improved over time in both populations, the disproportional effects remained. CONCLUSIONS: General COVID-19-related restrictions for the entire Dutch population affected people with MID or low literacy skills more negatively than the general population. Our study underscores the relevance of including these subpopulations in public health research because they are often overlooked in regular health data. An accessible web-based survey particularly targeted at this population enabled us to do so, and we reached a group of respondents significantly different from regular survey participants. This survey's results provided insights into the health of people with MID or low literacy skills and gained knowledge to be used by care organizations and policy makers to reduce health disparities during a pandemic and in general.
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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».