American Indian/Alaska Native community infrastructure limitations amid the COVID-19 threat
Bibliographic record
Abstract
Objective: The recent SARS-CoV-2 (COVID-19) pandemic that is spreading throughout the nation is a particular threat to American Indian and Alaska Native (AI/AN) communities. The use of recommended methods to prevent or mitigate the spread of the virus, such as hand washing, social distancing, masks, contact tracing and community education is highly problematic at many of these sites. The objective of this paper is to identify and examine structural or cultural barriers to implementing COVID-19 recommendations on select reservation sites.Methods: A qualitative approach that collected and analyzed data from existing sources including newsletter articles, relevant policies and other published reports was instituted in the Spring of 2020. The Centers for Disease Control and Prevention (CDC) policies regarding COVID-19 recommendations to halt the spread of the virus were selected as the standard for COVID-19 prevention, surveillance and mitigation. News articles between March 1, 2020 and December 1, 2020 were identified using various search engines and tribal websites. Information from news resources, including literature reviews, newsletter articles, social media reports, and tribal policy announcements, were gathered and reviewed. Two U.S. southwestern communities are used as examples for the review.Results: Data collected from various sources paint a picture of American Indian communities that lack adequate community infrastructures, and have problems of residential isolation, close living quarters, and contaminated and scarce water supplies. Unsafe or limited water restricts handwashing. Limited informational tools, such as telephone, internet, computer and newsletters, restricted adequate notification of the novel coronavirus to American Indian reservation communities. Often, the lack of a physical home address can create barriers to healthcare accessibility and surveillance, as it limits the identification and access to households. In addition, many traditional cultures of AI/ANs emphasize the interrelatedness of all in nature and thus require an ecological approach to health education and preventive measures, identified as a limitation for COVID-19 surveillance and mitigation.Conclusions: AI/AN communities face a serious threat of contracting COVID-19. Four key infrastructure limitations to effective COVID-19 prevention, surveillance and mitigation were identified: limited access to safe water, deficient telecommunication networks (telephone, internet, and television), housing isolation and shortages, and inadequate medical services – are experienced by many AI/AN communities. Although there are 574 federally recognized tribes in the United States, the two identified in this study subscribe to an ecological approach to health education and preventive measures in that they believe in the interrelatedness of all things in nature. Surveillance questions may be misunderstood or seem invasive and prevention measures (masks, social distancing, and handwashing) may seem to be extreme measures to groups so close to the environment. Together, these present serious barriers to prevention and mitigation of the COVID-19 virus in this underserved population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".