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Enregistrement W4417520336 · doi:10.1016/j.jenvp.2025.102894

Eco-emotions in children and adolescents: A rapid review of the qualitative literature

2025· article· en· W4417520336 sur OpenAlexafffund
Judy Wu, Martin Gina, Gómez Maya, Kaufmann Julia, Hasina Samji

Notice bibliographique

RevueJournal of Environmental Psychology · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueClimate Change Communication and Perception
Établissements canadiensBC Centre for Disease ControlSimon Fraser University
Organismes subventionnairesMinistry of Health, British Columbia
Mots-clésAngerHappinessQualitative researchMental healthPerceptionCoping (psychology)ApathyLearned helplessnessAnxiety

Résumé

récupéré en direct d'OpenAlex

The global environmental crisis, driven by climate change and environmental degradation (CCED), profoundly impacts the mental and emotional well-being of children and adolescents. This is of concern as children and adolescents are likely to have disproportionate and long-term CCED-related mental health impacts due to their developmental stage, limited influence over environmental decision-making, and long-term exposure to CCED impacts. Recent research has increasingly sought to understand the psychological impacts of emotions related to the awareness of CCED, referred to as eco-emotions. However, much of this research has focused predominantly on fear and anxiety, commonly termed climate- and eco-anxiety. To address the gap in understanding the broader spectrum of eco-emotions experienced by children and adolescent, a rapid review of qualitative literature was conducted. This rapid review searched six electronic databases and synthesized findings from 48 qualitative and mixed methods studies. Across many studies, emotions such as worry, fear, anxiety, and anger were reported by many participants. Emotions such as sadness, grief, powerlessness, and helplessness were also reported. While the eco-emotions experienced were predominantly found to be negative, eco-emotions such as hope, optimism, and happiness were also observed, particularly among those participating in CCED-focused programming (e.g., educational initiatives, community-based environmental projects, or programs explicitly addressing eco-emotions). Apathy toward CCED was noted in some cases and was often linked to perceptions about its relevance or perceived immunity to its impacts. Some studies reported on deleterious mental health and well-being impacts related to negative eco-emotions. Methods of coping with eco-emotions were also extracted and synthesized. Children and adolescents reported the use of problem-focused and emotion-focused coping strategies, such as engagement in pro-environmental behaviours and connecting with friends and family for support. In a small number of studies, meaning-focused strategies such as positive reframing of the global environmental crisis were also noted. Findings from this review highlight the need for eco-emotions research beyond climate- and eco-anxiety and underscore the importance of tailored CCED-programming to support children and adolescents experiencing negative eco-emotions. • Children and adolescents experience a wide range of eco-emotions in response to the global environmental crisis; worry, anxiety, anger, and frustration were reported in many studies. • Expanding research beyond eco-anxiety to include emotions like anger, sadness, powerlessness, and hope may provide deeper insights into behavioral and mental health impacts of climate change and environmental degradation. • Environmentally themed programming (e.g., educational programs, climate action programs) show promise to transform negative eco-emotions into hope and empowerment and may promote resilience and proactive engagement among youth.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,505
Score d'incertitude au seuil0,282

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,134
Tête enseignante GPT0,482
Écart entre enseignants0,348 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission2
Résumé présentoui

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