Avoidance of medical care among American Indians with a history of cancer during the coronavirus pandemic
Bibliographic record
Abstract
Objectives: Assess the percentage of cancer-related appointment delays, cancelations, and the unavailability of medications experienced by American Indian participants during the COVID-19 pandemic. Methods: This cross-sectional survey study was completed between October 2020 and July 2021 by 360 individuals with cancer who lived in California and Oklahoma. Binary and multivariate logistic regression analysis was completed in SAS 9.4. Results: During the initial Covid-19 pandemic, almost one-third (30%) of respondents delayed cancer-related appointments, 42% canceled cancer-related appointments, and one-quarter (24%) were unable to access prescription medications or over-the-counter medications (27%) due to COVID-19. People who underwent testing for COVID-19 were five times more likely to delay a medical appointment [adjusted odds ratio (aOR) = 5.3, 95% CI:2.4, 11.7] and people who followed three or more social distancing measures were more than six times more likely to cancel medical appointments (aOR:6.3, 95% CI:2.9, 13.9). Conclusion: This study identifies delays, cancelations, and medication inaccessibility people identifying as American Indian faced during the coronavirus pandemic. Disparities in healthcare delivery could contribute to increased morbidity and mortality rates of cancer.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".