COVID-19 Communication From Seven Health Care Institutions in North Texas for English- and Spanish-Speaking Cancer Patients: Mixed Method Website Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".