Continuity of Care Among Postmenopausal Women With Cardiometabolic Diseases in the United States Early During the COVID-19 Pandemic: Findings From the Women’s Health Initiative
Bibliographic record
Abstract
BACKGROUND: In response to the COVID-19 pandemic, public health measures, including stay-at-home orders, were widely instituted in the United States by March 2020. However, few studies have evaluated the impact of these measures on continuity of care among older adults living with chronic diseases. METHODS: Beginning in June 2020, participants of the national Women's Health Initiative (WHI) (N = 64 061) were surveyed on the impact of the pandemic on various aspects of their health and well-being since March 2020, including access to care appointments, medications, and caregivers. Responses received by November 2020 (response rate = 77.6%) were tabulated and stratified by prevalent chronic diseases, including hypertension, type 2 diabetes, and cardiovascular disease (CVD). RESULTS: Among 49 695 respondents (mean age = 83.6 years), 70.2% had a history of hypertension, 21.8% had diabetes, and 18.9% had CVD. Half of the respondents reported being very concerned about the pandemic, and 24.5% decided against seeking medical care to avoid COVID-19 exposure. A quarter reported difficulties with getting routine care, and 45.5% had in-person appointments converted to telemedicine formats; many reported canceled (27.8%) or rescheduled (37.7%) appointments. Among those taking prescribed medication (88.0%), 9.7% reported changing their method of obtaining medications. Those living with and without chronic diseases generally reported similar changes in care and medication access. CONCLUSIONS: Early in the pandemic, many older women avoided medical care or adapted to new ways of receiving care and medications. Therefore, optimizing alternative services, like telemedicine, should be prioritized to ensure that older women continue to receive quality care during public health emergencies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".