COVID-19 Communication From Seven Health Care Institutions in North Texas for English- and Spanish-Speaking Cancer Patients: Mixed Method Website Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has created an urgent need to rapidly disseminate health information, especially to those with cancer, because they face higher morbidity and mortality rates. At the same time, the pandemic's disproportionate impact on Latinx populations underscores the need for information to reach Spanish speakers. However, the equity of COVID-19 information communicated through institutions' online media to Spanish-speaking cancer patients is unknown. OBJECTIVE: We conducted a multimodal, mixed method document review study to evaluate the equity of online information about COVID-19 and cancer available to English- and Spanish-speaking populations from seven health care institutions in North Texas, where one in five adults is Spanish-speaking. Our focus was less on the "digital divide," which conveys disparities in access to computers and the internet based on the race/ethnicity, education, and income of at-risk populations; rather, our study asks the following question: to what extent is online content useful and culturally appropriate in meeting Spanish speakers' information needs? METHODS: We reviewed 50 websites (33 English and 17 Spanish) over a period of 1 week in the middle of May 2020. We sampled seven institutions' main oncology and COVID web pages, and both internal (institutional) and external (noninstitutional) linked content. We conducted several analyses for each sampled page, including (1) thematic content analysis, (2) literacy level analysis using Readability Studio software, (3) coding using the Patient Education and Materials Assessment Tool (PEMAT), and (4) descriptive analysis of video and diversity content. RESULTS: The themes most frequently addressed on English and Spanish websites differed. While "resources/FAQs" were frequently cited themes on both websites, English websites more frequently addressed "news/updates" and "cancer+COVID," and Spanish websites addressed "protection" and "COVID data." Spanish websites had on average a lower literacy level (11th grade) than English websites (13th grade), although still far above the recommended guideline of 6th to 8th grade. The PEMAT's overall average accessibility score was the same for English (n=33 pages) and Spanish pages (n=17 pages) at 82%. Among the Dallas-Fort Worth organizations, the average accessibility of Spanish pages (n=7) was slightly lower than that of English pages (n=19) (77% vs 81%), due mostly to the discrepancy in English-only videos and visual aids. Of the 50 websites, 12 (24%) had embedded videos; however, 100% of videos were in English, including one on a Spanish website. CONCLUSIONS: We identified an uneven response among the seven health care institutions for providing equitable information to Spanish-speaking Dallas-Fort Worth residents concerned about COVID and cancer. Spanish speakers lack equal access in both diversity of content about COVID-19 and access to other websites, leaving an already vulnerable cancer patient population at greater risk. We recommend several specific actions to enhance content and navigability for Spanish speakers.
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 | 0,012 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 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 ».