“It’s Not Something We Like to Think About Because It’s So Devastating”: Understanding Eastern Canadian Young Women’s Mental Health in Our Changing Climate
Bibliographic record
Abstract
Women are disproportionately affected by climate change, yet even though mental health and climate change is an emerging field, little research focuses on their mental health. The purpose of this study was to explore young women’s perceptions of climate change, gender, and mental health. A feminist poststructural (FPS) approach guided this research. FPS and discourse analysis were used to explore nine participants’ perceptions of their mental health in relation to the changing climate, and how their experiences were personally, socially, and institutionally constructed. Findings highlight participant relationships to discourses surrounding hopelessness, anxiety, grief and frustration, intersectionality, stereotypes, and gender-based violence (GBV). Study findings supported by broader literature provide recommendations for the discipline of health promotion regarding gender appropriate climate mitigation and adaptation strategies that prioritize and recognize mental health. We urge health promotion to recognize and integrate the fact that climate change amplifies existing inequities into health and climate change policies whenever possible. Climate change and health policies should ensure women are safe and protected before climate driven weather events to prevent instances of GBV. We recommend that health promotion media specialists recognize the dangers and ineffectiveness of fear mongering and attempt to promote climate solutions as opposed to only stories of despair and ecological degradation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".