A Scoping Review of International Literature on Patient-Provider Satisfaction with Virtual Prenatal Appointments: Recommendations for Canadian Providers
Bibliographic record
Abstract
While Canadian telemedicine has grown in popularity following the COVID-19 pandemic, research on patient and provider satisfaction with these services is scarce, especially in its application in prenatal care delivery. Moreover, majority of existing literature focuses on clinical outcomes when investigating prenatal telemedicine’s success, leaving a gap in our understanding of patient-provider satisfaction. Literature examining attitudes towards virtual prenatal appointments consistently notes low satisfaction and rarely offers recommendations to improve care. This review aims to investigate determinants of and barriers to patient-provider satisfaction with virtual prenatal appointments and offer recommendations to improve Canadian satisfaction. Searches were run on PubMed and CINAHL using keywords including “prenatal”, “virtual”, “satisfaction”, and “appointment”. The retrieved literature was uploaded and screened in Covidence. In total, 43 papers covering virtual prenatal care in jurisdictions outside of Canada were reviewed for data extraction. The literature was summarized alongside six key themes: logistical barriers, non-logistical barriers, patient-provider communication, appointment types, general benefits of telehealth, and suggestions for improvement. Leading determinants were found to be the level of perceived and actual barriers to utilization, quality of patient-provider communication and relationships, and access to devices and internet connectivity. Recommendations for improving satisfaction with Canadian care include cross-border consultations, use of provider care teams, and improved telehealth management and provider training. The focus on international research enabled us to identify what lessons Canadian practitioners can learn from other countries in the provision of virtual prenatal care. This review also contributes to the scarce Canadian research on satisfaction with virtual prenatal appointments.
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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 | 0.037 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.040 | 0.054 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".