Nurses are Key Members of the Abortion Care Team: Why aren’t Schools of Nursing Teaching Abortion Care?
Bibliographic record
Abstract
Abortion is a common and safe procedure in Canada, with the Canadian Institute for Health Information reporting approximately 100,000 procedures per year. Yet access remains problematic. As abortion is unrestricted by criminal law in Canada, access is limited by geographic barriers and by a shortage of providers. We present a feminist critical lens to describe how the marginalization of nursing and nurses in abortion care contributes to social stigma and public misunderstanding about abortion access. The roles of registered nurses and nurse practitioners in abortion advocacy, service navigation, counselling, education, support, physiological care and follow up are underutilized and under-researched. In 2015, decades after its availability elsewhere in the world, Health Canada approved mifepristone (a pill for medical abortion). In 2017, provincial regulators began to authorize nurse practitioners to independently provide medical abortion care, as appropriate given the inclusion in nurse practitioner scope of practice to order diagnostic tests, make diagnoses, and treat health conditions. Ensuring nurse practitioners are able to practice medical abortion has the potential to significantly increase abortion access for rural, remote and other marginalized populations. There is also an opportunity to optimize the registered nurse role in abortion care. However, achieving these improvements is challenging as abortion is not routinely taught in Canadian Schools of Nursing. We argue that to destigmatize abortion and improve access, undergraduate nursing and nurse practitioner programs across the country must begin to include abortion and family planning competencies.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.049 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".