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Record W3157169461 · doi:10.3390/ijerph18094573

The Interplay between Social and Ecological Determinants of Mental Health for Children and Youth in the Climate Crisis

2021· review· en· W3157169461 on OpenAlexafffund
Maya Gislason, Angel Kennedy, Stephanie Witham

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsSimon Fraser University
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMental healthPsychologyWorryClimate changeDistressAnxietyDisconnectionFeelingThematic analysisDevelopmental psychologyPerceptionSadnessEcologySocial psychologyClinical psychologySociologyQualitative researchPsychiatryPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Children and youth are showing increasing levels of mental health distress due to the climate crisis, characterized by feelings of sadness, guilt, changes in sleep and appetite, difficulty concentrating, solastalgia, and disconnection from land. To gain a deeper understanding of the relationship between climate change and children and youth's mental health, we conducted a rapid review and a thematic analysis of the results in NVivo 12. Our findings show that children and youth experience a plethora of direct and indirect effects from climate change and this impacts their mental wellbeing in diverse and complex ways. Young people also have varied perceptions of climate change based on their social locations and many are dealing with feelings of immense worry and eco-anxiety. The mental health impacts of climate change on children/youth are tied to Social Determinants of Health (SDoH) but also need to be understood in relation to the Ecological Determinants of Health (EDoH). Through an eco-social lens, this paper explores these conceptual issues and uses them to provide a framework for understanding the interplay of social and ecological determinants of mental health for children/youth.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.255
GPT teacher head0.515
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations135
Published2021
Admission routes2
Has abstractyes

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