Women’s Satisfaction With Telehealth Services During The COVID-19 Pandemic: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Since March 2020, the need to reduce patients' exposure to COVID-19 has resulted in a large-scale pivot to telehealth service delivery. Although studies report that pregnant women have been generally satisfied with their prenatal telehealth experiences during the pandemic, less is known about telehealth satisfaction among postpartum women. OBJECTIVE: This study examined telehealth satisfaction among both pregnant and recently pregnant women during the COVID-19 pandemic, to determine whether demographic factors (ie, race, age, marital status, education level, household income, and employment status) are associated with telehealth satisfaction in this population. METHODS: A web-based cross-sectional survey designed to capture data on health-related behaviors and health care experiences of pregnant and recently pregnant women in the United States was disseminated in Spring 2022. Eligible participants were at least 18 years old, identified as a woman, and were currently pregnant or had been pregnant in the last 3 years. RESULTS: In the final analytic sample of N=403, the mean telehealth satisfaction score was 3.97 (SD 0.66; score range 1-5). In adjusted linear regression models, being aged 35-44 years (vs 18-24 years), having an annual income of ≥ US $100,000 (vs < US $50,000), and being recently (vs currently) pregnant were associated with greater telehealth satisfaction (P≤.049). CONCLUSIONS: Although perinatal women are generally satisfied with telehealth, disparities exist. Specifically, being aged 18-24 years, having an annual income of < US $50,000, and being currently pregnant were associated with lower telehealth satisfaction. It is critical that public health policies or programs consider these factors, especially if the expanded use of telehealth is to persist beyond the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".