Patient perceptions of the benefits and barriers of virtual postnatal care: a qualitative study
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".