Climate change emotions, perceived mental health impacts, and supports among adolescents in Dhaka, Bangladesh: A cross-sectional mixed methods exploratory study
Bibliographic record
Abstract
Introduction: Bangladesh faces frequent climate change-related extreme weather events. This study explores Bangladeshi adolescents' emotional responses to climate change and their perceived impact of climate change on their mental health. The supports they currently use and wish to have for coping with climate change related difficult thoughts and feelings are also explored. Materials & Methods: = 200; aged 1-18 years old, mean age 17.2 years, SD=1.17) from two English-speaking schools in Dhaka, Bangladesh completed an anonymous survey. Open and close-ended survey measures assessed multiple climate change emotions, climate change worry, perceived mental health impacts of climate change, and current and desired supports. Descriptive analyses were performed, and qualitative responses were examined through thematic analysis. Following a mixed methods convergent parallel design approach, qualitative and quantitative data were integrated together at interpretation. Results: Participants reported that they experienced a wide range of climate emotions, including concern (85 %), sadness (74 %), anger (63 %), guilt (63 %), and fear (63 %). Additionally, 62 % of participants indicated that they perceive their mental health has been impacted by climate change, either a lot or a little. Open-ended responses revealed that adolescents perceive climate change as impacting their mental health in multiple ways, including through negative emotions, physical symptoms, and reduced motivation. The most commonly used supports were self-education (52 %), school-based programs/clubs (51 %), and conversations with others (45 %). Participants expressed wishing they had more access to community-based programs/clubs (58 %) as well as climate action activities they could do independently (52 %). Conclusion: This exploratory study highlights that Bangladeshi adolescents may be experiencing a range of negative emotions and mental health impacts as a result of climate change. These findings are consistent with studies from other regions. While the sample was limited to students in English-speaking schools, the results can inform climate change risk mitigation and adaptation strategies. Future research should prioritize expanding to other settings in Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".