A Qualitative Examination of Patient Experiences and Determinants of Virtual Postnatal Follow-Up Visits
Bibliographic record
Abstract
Abstract Objective: This study aimed to understand the perceptions of new mothers using virtual care in the form of video conferencing to gain insight into the social and environmental determinants that could potentially impact compliance for post-natal follow-up visits.Methods: Semi-structured interviews were conducted with 15 patients of Kingston Health Sciences Centre. The interviews were 20-25 minutes in length and recorded through an audio recorder. Thematic analysis was conducted in order to derive the major themes explored in this study.Results: In general, new mothers reported high satisfaction with virtual care, emphasizing benefits related to comfort, convenience, communication, socioeconomic factors, and the ease of technology use.Conclusions: Not only can virtual care address many of the barriers that new mothers face in accessing in-person healthcare services, but virtual care can also elucidate various social and environmental determinants responsible for facilitating access to postnatal follow-up care. Further research and clinical endeavours should focus on these various determinants (and the ways they intersect) and how they underpin patient perceptions of virtual and in-person care. Such a lens not only addresses the struggle of long-term patient compliance to maternal health care, but will additionally shed light on how to make obstetric care more equitable.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".