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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".