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Record W3188138218 · doi:10.1186/s12884-021-03999-9

Patient perceptions of the benefits and barriers of virtual postnatal care: a qualitative study

2021· article· en· W3188138218 on OpenAlexafffund
Megan Saad, Sophy Chan-Nguyen, Lisa Nguyen, Siddhartha Srivastava, Ramana Appireddy

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsQueen's University
FundersPhysicians' Services Incorporated Foundation
KeywordsThematic analysisNursingMedicinePerceptionQualitative researchUsabilityPatient satisfactionHealth careMedical educationPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study is to understand the perceptions of new mothers using virtual care via video conferencing to gain insight into the benefits and barriers of virtual care for obstetric patients. METHODS: Semi-structured interviews were conducted with 15 patients attending the Kingston Health Sciences Centre. The interviews were 20-25 min in length and recorded through an audio recorder. Thematic analysis was conducted in order to derive the major themes explored in this study. RESULTS: New mothers must often adopt new routines to balance their needs and their child's needs. These routines could impact compliance and motivation to attend follow-up care. In our study, participants expressed high satisfaction with virtual care, emphasizing benefits related to comfort, convenience, communication, socioeconomic factors, and the ease of technology use. Participants also perceived that they could receive emotional support and build trust with their health care providers despite the remote nature of their care. Due to its ease of use and increased accessibility, we argue that virtual care shows promise to facilitate long-term compliance to care in obstetric patients. CONCLUSIONS: Virtual care is a useful modality that could improve compliance to obstetric care. Further research and clinical endeavours should examine how social factors and determinants intersect to determine how they underpin patient perceptions of virtual and in-person care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.319
Teacher spread0.302 · 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 designObservational
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

Citations53
Published2021
Admission routes2
Has abstractyes

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