What motivates primary care providers to prescribe mifepristone medication abortion? Results of a qualitative investigation in Canada
Bibliographic record
Abstract
BACKGROUND: Mifepristone-misoprostol, the gold standard medication abortion drug regimen, became available in Canada in 2017. However, there is limited evidence regarding the factors that influence primary care providers to begin prescribing medication abortion. We aimed to explore perspectives of the behavioural, social, and system factors that influence implementation of medication abortion prescribing among primary care providers in Canada. METHODS: We led a qualitative investigation involving one-on-one interviews with primary care providers who were interested in becoming or already were low-volume medication abortion prescribers in Canada. We collected data at two time points: (1) in 2018 after the first year of mifepristone's availability and (2) in 2023. We recruited participants through partner health organizations' online platforms and listservs. We conducted reflexive thematic analysis to understand resolved, novel, and ongoing factors influencing the implementation of mifepristone in primary care and mapped our results to Diffusion of Innovation theory. RESULTS: We completed 18 interviews with primary care providers from across Canada. We identified 5 core Diffusion of Innovation factors that were important to primary care provider implementation of medication abortion care. These factors included adoption and assimilation (motivation), where prescriber pro-choice attitudes and commitment to provide abortion as part of generalist primary care were facilitators. The innovation (knowledge required to use it) and implementation (external collaboration) were interrelated constructs: after training in the knowledge and skills to offer medication abortion, prescribers needed ongoing collaboration and support with physician and pharmacist peers. System antecedents (a receptive context for change) included challenges with abortion-related stigma and harassment in professional and community settings. Finally, system readiness (dedicated time and resources) was necessary to ensure ease in the logistics of medication abortion care, including billing, counseling, and delays in timely care. CONCLUSIONS: Our results highlight that, after five years, barriers still exist to providing mifepristone medication abortion in Canadian primary care. We illustrate the importance of addressing ongoing perceptions of logistical barriers to care, concerns about advertising abortion services to the community, and the need for robust mentorship and consultation pathways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".