Advancing Nursing Education Through an Intersectionality-Framed Approach: An Exemplar With Women Living at the Epicentre of Gender-Based Violence, Climate Crises, Racism, and Migration Status
Bibliographic record
Abstract
Changes to Earth’s biosphere causing intensification of climate crises worldwide are rapidly affecting human health. Advancing the acceleration of globalization and increased flows of migration, climate change contributes to rapid population change and increased population complexities in Canada, requiring nursing students to be better prepared to deliver care that is relationally ethical, action-oriented, and centred on mobilizing our social mandates of equity and justice. Women living at the crossroads of gender-based violence, racism, and migration status are one such complex population that is increasing exponentially due to geopolitical fragility and multiple global climate crises. Over 140 million women currently experience forced international migration, driven majorly by eroding ecosystems. Climate crises disproportionately affect women forced to migrate through exacerbating their exposure to violence, diminished social networks, limited access to adequate health care, and racial discrimination. To foster nursing student readiness for practice and leadership aimed at an increasingly complex population demographic, we propose framing nursing pedagogy with intersectional environmentalism as an approach that builds on planetary health principles and the social and environmental justice mandates of the nursing discipline. We respond to at least two significant global and national challenges of the 21st century, including the health impacts of climate crises and the inequitable health experiences among women living at the axes of forced migration, gender-based violence, and systemic racism. Our discussion situates women living at these intersections within the context of climate change to promote the examination of the disparate impacts on women’s health and well-being. In situating intersectionality as described by Collins (1990, 2019), Crenshaw (1991), and the Combahee River Collective (1977, 1981), we draw attention to the need to tackle racism, sexism, and classism. We engage with intersectional environmentalism (Thomas, 2022) as a critical approach to inclusively engage with the complexity of how climate threats affect the health of women living simultaneously with gender-based violence, racism, and migration status. Rather than providing stand-alone curriculum guidance, we propose two strategies for integrating across nursing undergraduate and graduate curriculum objectives. These strategies involve examining local and global histories of colonialism and consequential impacts on the axes of environment and the health of women forced to migrate, and centring women forced to migrate through the concept of climate optimism as framed by intersectional environmentalism.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| 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".