Telemedicine for First-Trimester Medical Abortion in Canada: Results of a 2019 Survey
Bibliographic record
Abstract
Introduction: Telemedicine has the potential to improve abortion access disparities in Canada. We aimed to explore the provision of telemedicine for first-trimester medical abortion and related barriers in 2019. Methods: We conducted a national, cross-sectional, anonymized, web-based survey of clinicians who provided abortion care in 2019 in Canada. We distributed our survey through professional health organizations to maximize identification of possible eligible respondents and used a modified Dillman technique to foster responses. Questions elicited provider demographics, clinical characteristics, including telemedicine first-trimester medical abortion and perceived related barriers. Descriptive statistics were analyzed using R software. Results: Among 465 respondents, 388 reported providing first-trimester medical abortion across Canada; 44.0% reported experience using telemedicine for some components of care: 49.3% of primary care clinicians and 28.7% of specialists. Telemedicine was used for initial consultation (86.0%), prescription (82.2%), or follow-up (92.2%). The median percentage of telemedicine providers' patients who underwent a dating ultrasound was 90.0. The majority usually followed up with patients through quantitative human chorionic gonadotropin (hCG) (84.2%). Seventy-eight percent perceived barriers to telemedicine; the most common being inability to confirm gestational age with ultrasound (43.0%), and lack of provincial telemedicine abortion fee code to pay practitioners (30.2%), timely access to serum hCG testing (24.6%), and nearby emergency services (23.3%). Discussion: In 2019, fewer than half of respondents reported providing some aspects of first-trimester medical abortion through telemedicine and the majority perceived barriers. Our results can inform knowledge translation activities to reduce barriers and increase telemedicine abortion care in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".