Care providers’ experiences with and attitudes towards virtual antenatal care: Findings from a qualitative study in British Columbia
Bibliographic record
Abstract
Background: Virtual care has emerged as an adjunctive response to challenges in rural health care, including maternity care, and use has accelerated during the coronavirus disease 2019 (COVID-19) pandemic. This gives rise to the need for a strategic plan for post-COVID-19 virtual maternity care in rural communities. To date, no provincial initiative has focused on understanding and documenting the needs of maternity care practitioners to provide virtual care. Methods: Qualitative study, including virtual interviews and focus groups with rural primary maternity care providers and urban and rural specialists on perceptions of the utility of virtual maternity care pre- and post-COVID-19, and benefits and barriers of virtual care. Data were thematically analysed. Results: In total, 82 health care providers participated in the study. Health care provider responses fell into three categories: Attributes of virtual care, barriers to virtual care and system interventions needed to optimize the provision of virtual perinatal care. Participants expressed a desire for use of virtual communication tools post-COVID-19, continued ability to use fee codes for virtual care and a need for more secure texting options. The benefits of tripartite consultations were noted by many participants; impacts of the transition to virtual care included additional workload and interrupted workflow. Concerns over the lack of physical examinations and challenges in building relationships with patients when providing virtual care were frequently noted. Conclusion: Adapting the current implementation of virtual maternity care in British Columbia may be enhanced through several provider- and evidence-derived strategies, many of which are currently underway in BC. The results from this provincial survey will be used to focus further discussion on the characteristics of an optimal system to meet patient and provider needs within a rural context.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".