The Double-Edged Sword of Online Learning for Ethnoracial Differences in Adolescent Mental Health During Late Period of the COVID-19 Pandemic in the United States: National Survey
Notice bibliographique
Résumé
BACKGROUND: Despite several theories suggesting online learning during the COVID-19 pandemic would aggravate ethnoracial disparities in mental health among adolescents, extant findings suggest no ethnoracial differences in mental health or that those from minoritized ethnoracial groups reported better mental health than their White counterparts. OBJECTIVE: This study aimed to identify why findings from prior studies appear to not support that ethnoracial disparities in mental health were aggravated by testing 2 pathways. In pathway 1 pathway, online learning was associated with reporting fewer confidants, which in turn was associated with poorer mental health. In pathway 2, online learning was associated with reporting better sleep, which in turn was associated with better mental health. METHODS: We analyzed survey data from a US sample (N=540) of 13- to 17-year-olds to estimate how school modality was associated with mental health via the 2 pathways. The sample was recruited from the AmeriSpeak Teen Panel during spring of 2021, with an oversample of Black and Latino respondents. Ethnoracial categories were Black, Latino, White, and other. Mental health was measured with the 4-item Patient Health Questionnaire, which assesses self-reported frequency of experiencing symptoms consistent with anxiety and depression. School modality was recorded as either fully online or with some in-person component (fully in-person or hybrid). We recorded self-reports of the number of confidants and quality of sleep. Covariates included additional demographics and access to high-speed internet. We estimated bivariate associations between ethnoracial group membership and both school modality and mental health. To test the pathways, we estimated a path model. RESULTS: Black and Latino respondents were more likely to report being in fully online learning than their White counterparts (P<.001). Respondents in fully online learning reported fewer confidants than those with any in-person learning component (β=-.403; P=.001), and reporting fewer confidants was associated with an increased likelihood of reporting symptoms consistent with anxiety (β=-.121; P=.01) and depression (β=-.197; P<.001). Fully online learning respondents also reported fewer concerns of insufficient sleep than their in-person learning counterparts (β=-.162; P=.006), and reporting fewer concerns was associated with a decreased likelihood of reporting symptoms consistent with anxiety (β=.601; P<.001) and depression (β=.588; P<.001). Because of these countervailing pathways, the total effect of membership in a minoritized ethnoracial group on mental health was nonsignificant. CONCLUSIONS: The findings compel more nuanced discussions about the consequences of online learning and theorizing about the pandemic's impact on minoritized ethnoracial groups. While online learning may be a detriment to social connections, it appears to benefit sleep. Interventions should foster social connections in online learning and improve sleep, such as implementing policies to enable later start times for classes. Future research should incorporate administrative data about school modality, rather than relying on self-reports.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».