The Successes and Challenges of Implementing Telehealth for Diverse Patient Populations Requiring Prenatal Care During COVID-19: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Although telehealth appears to have been accepted among some obstetric populations before the COVID-19 pandemic, patients' receptivity and experience with the rapid conversion of this mode of health care delivery are unknown. OBJECTIVE: In this study, we examine patients' prenatal care needs, preferences, and experiences during the COVID-19 pandemic, with the aim of supporting the development of successful models to serve the needs of pregnant patients, obstetric providers, and health care systems during this time. METHODS: This study involved qualitative methods to explore pregnant patients' experiences with prenatal health care delivery at the onset of the COVID-19 pandemic. We conducted in-depth interviews with pregnant patients in the first and second trimester of pregnancy who received prenatal care in Cleveland, Ohio, from May to July 2020. An interview guide was used to probe experiences with health care delivery as it rapidly evolved at the onset of the pandemic. RESULTS: Although advantages of telehealth were noted, there were several concerns noted with the broad implementation of telehealth for prenatal care during the pandemic. This included concerns about monitoring the pregnancy at home; the need for additional reassurance for the pregnancy, given the uncertainties presented by the pandemic; and the ability to have effective patient-provider discussions via a telehealth visit. The need to tailor telehealth to prenatal health care delivery was noted. CONCLUSIONS: Although previous studies have demonstrated that telehealth is a flexible and convenient alternative for some prenatal appointments, our study suggests that there may be specific needs and concerns among the diverse patient groups using this modality during the pandemic. More research is needed to understand patients' experiences with telehealth during the pandemic and develop approaches that are responsive to the needs and preferences of patients.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".