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Record W4309159258 · doi:10.2196/38821

Impact of Telehealth on the Delivery of Prenatal Care During the COVID-19 Pandemic: Mixed Methods Study of the Barriers and Opportunities to Improve Health Care Communication in Discussions About Pregnancy and Prenatal Genetic Testing

2022· article· en· W4309159258 on OpenAlexvenueno aff
Caitlin Craighead, Christina Collart, Richard M. Frankel, Susannah Rose, Anita D. Misra‐Hebert, Brownsyne Tucker Edmonds, Marsha Michie, Edward K. Chien, Marissa Coleridge, Oluwatosin Goje, Angela C. Ranzini, Ruth M. Farrell

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthThematic analysisPrenatal carePandemicMedicinePregnancyHealth careNursingTelemedicineQualitative researchFamily medicineCoronavirus disease 2019 (COVID-19)PsychologyPopulationEnvironmental healthDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic brought significant changes in health care, specifically the accelerated use of telehealth. Given the unique aspects of prenatal care, it is important to understand the impact of telehealth on health care communication and quality, and patient satisfaction. This mixed methods study examined the challenges associated with the rapid and broad implementation of telehealth for prenatal care delivery during the pandemic. OBJECTIVE: In this study, we examined patients' perspectives, 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: Pregnant patients who received outpatient prenatal care in Cleveland, Ohio participated in in-depth interviews and completed the Coronavirus Perinatal Experiences-Impact Survey (COPE-IS) between January and December 2021. Transcripts were coded using NVivo 12, and qualitative analysis was used, an approach consistent with the grounded theory. Quantitative data were summarized and integrated during analysis. RESULTS: Thematic saturation was achieved with 60 interviews. We learned that 58% (35/60) of women had telehealth experience prior to their current pregnancy. However, only 8% (5/60) of women had used both in-person and virtual visits during this pregnancy, while the majority (54/60, 90%) of women participated in only in-person visits. Among 59 women who responded to the COPE-IS, 59 (100%) felt very well supported by their provider, 31 (53%) were moderately to highly concerned about their child's health, and 17 (29%) reported that the single greatest stress of COVID-19 was its impact on their child. Lead themes focused on establishing patient-provider relationships that supported shared decision-making, accessing the information needed for shared decision-making, and using technology effectively to foster discussions during the COVID-19 pandemic. Key findings indicated that participants felt in-person visits were more personal, established greater rapport, and built better trust in the patient-provider relationship as compared to telehealth visits. Further, participants felt they could achieve a greater dialogue and ask more questions regarding time-sensitive information, including prenatal genetic testing information, through an in-person visit. Finally, privacy concerns arose if prenatal genetic testing or general pregnancy conversations were to take place outside of the health care facility. CONCLUSIONS: While telehealth was recognized as an option to ensure timely access to prenatal care during the COVID-19 pandemic, it also came with multiple challenges for the patient-provider relationship. These findings highlighted the barriers and opportunities to achieve effective and patient-centered communication with the continued integration of telehealth in prenatal care delivery. It is important to address the unique needs of this population during the pandemic and as health care increasingly adopts a telehealth model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.164
GPT teacher head0.513
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2022
Admission routes1
Has abstractyes

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