Climate change emotions, perceived mental health impacts, and supports among adolescents in Dhaka, Bangladesh: A cross-sectional mixed methods exploratory study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».