Applying an intersectional climate justice lens to understand climate crisis impacts on sexual and reproductive health and rights and identify local solutions: Qualitative findings from Khulna, Bangladesh
Bibliographic record
Abstract
BACKGROUND: Climate change has been described as the greatest health threat of the 21st century. Increased evidence of the linkages between climate change and sexual and reproductive health and rights (SRHR) is essential to achieving full realization of SRHR. OBJECTIVE: To understand if and how women and girls' perceived climate vulnerability impacts their SRHR decision-making, behaviors, and outcomes in cyclone-affected communities in coastal Khulna, Bangladesh, we conducted qualitative research using an intersectional climate justice lens. DESIGN: Climate justice states that the climate crisis is not just an environmental or health problem, it is equally a political and social problem, whereby different communities feel the consequences differently, unevenly, and disproportionately depending on a multitude of factors shaped by intersecting systems of power and oppression. We adopted an intersectional climate justice lens to explore how women and girls' intersecting identities impact their experiences with climate change - particularly extreme weather events - and impact their perceived vulnerability. We employed a two-phased participatory qualitative research design. METHODS: = 49). Transcripts, activity outputs, and field notes were transcribed verbatim in Bangla, translated to English, and subsequently coded and analyzed thematically using Dedoose. RESULTS: Participants perceived numerous SRHR outcomes to be worsened by the climate crisis, including unintended pregnancy, sexual and gender-based violence, and pregnancy complications. Impacts were experienced differently across social categories, with overlapping identities including age, marital status, and religion magnifying vulnerability and risks to SRHR. Participants identified comprehensive SRHR and advances toward gender equity as essential for building climate resilience. CONCLUSION: Our findings provide actionable recommendations to support the full realization of climate justice and SRHR.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".