Nurses are Key Members of the Abortion Care Team: Why aren’t Schools of Nursing Teaching Abortion Care?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 |
| 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 it