Factors that influence Canadian primary care providers’ decision to prescribe medical abortion
Bibliographic record
Abstract
Purpose. To identify the factors that influence primary care providers in their decision to provide medical abortions. Background. Medical abortion has been commercially available in Canada since 2017, with reduced restrictions on prescribing since 2019. It is a safe and effective option for induced abortion and provides autonomy to pregnant people. Understanding the barriers that exist for primary care providers can help to identify ways to further incorporate medical abortion into practice and increase accessibility for patients. Design. Integrative review. Data sources. Studies were obtained through a search of the electronic databases CINAHL (EBSCO), Medline (OVID), and Google Scholar. Review Methods. The Critical Skills Appraisal Programme (CASP, 2023) checklist was modified to appraise all studies. Themes and study characteristics were elicited for data synthesis. Results. Eight studies were selected for review using inclusion and exclusion criteria. The themes identified were the availability of a community of practice, health equity, educational exposure, stigma, regulatory and funding issues, and interprofessional collaboration. Conclusions. Addressing the themes identified through careful consideration of policy implementation, exposure to medical abortion practice in training, ensuring a community of practice and interprofessional collaboration are important factors in increasing access to medical abortion.,
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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 | 0.009 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".