Patient Perception of Telehealth Prenatal Care During the Early Part of the COVID-19 Pandemic [A310]
Bibliographic record
Abstract
INTRODUCTION: Routine prenatal visits were rapidly converted to telehealth (TH) visits during the beginning of the COVID-19 pandemic. Patient perceptions and experience with TH were surveyed. Demographic factors were assessed to see if there were any predictors of satisfaction. METHODS: Institutional Review Board approval was obtained. Electronic as well as paper surveys were distributed to 2,012 women who had experienced virtual prenatal visits from March 2020 to June 2020. Responses were stored on a password-protected computer file and analyzed using Pearson Chi-square test and Fisher’s exact test. RESULTS: A total of 333 (17%) women completed the survey (235 online and 98 paper). The majority of respondents agreed that TH made it easier to see their obstetrical provider during the onset of the pandemic (80%). Although 83% agreed that it is important to offer a TH program, 78% agreed that they would have been willing for in-person visits during the height of the pandemic. Only about one quarter (26%) of the patients reported that they would rather use TH versus coming in-person for a visit, and only 38% thought that TH interactions were equal to in-person visits. Medicaid patients (86%) indicated higher overall satisfaction with TH than commercially insured patients (69%; P=.009). CONCLUSION: Respondents were overall satisfied with TH prenatal care including ease in seeing and talking to their provider. Medicaid patients were more satisfied than commercially insured patients. In geographic areas with provider shortages, TH prenatal care may be an acceptable modality to deliver care, especially among patients insured with Medicaid.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".