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Record W4387901173 · doi:10.1093/eurpub/ckad160.1103

Canadian Adolescents Perceptions of Survey Questions about Climate Emotions and Coping Strategies

2023· article· en· W4387901173 on OpenAlexaffabout
Alina Cosma, Tasha Roswell, Margaret A. Treble, Gina Martin

Bibliographic record

VenueEuropean Journal of Public Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsAthabasca University
Fundersnot available
KeywordsWorryPsychologyCoping (psychology)PerceptionThematic analysisFocus groupBrainstormingClimate changePreferenceSocial psychologyDevelopmental psychologyApplied psychologyClinical psychologyQualitative researchAnxietySociologySocial science

Abstract

fetched live from OpenAlex

Abstract Background Globally, there is increased research and media attention about how young people are experiencing climate change. Much of this attention is about the worry and concern young people feel about climate change, with recent survey research finding that the majority of young people are at least moderately worried about climate change. Objectives To examine the perceptions that Canadian adolescents have about survey measures that have been used to study climate emotions and the coping strategies they use. Methods Seven online focus groups occurred in December 2022 with Canadian adolescents aged 15-18. The focus groups began with a brainstorming activity followed by a semi-structured discussion about climate emotion survey questions that have been used in previous surveys. Data were analyzed using rapid thematic analysis. Results Overall, Canadian adolescents showed a preference for questions that allowed for more nuance in their response. Questions that were seen to allow for a variety of perspectives were also favored. Conclusions Findings from this study can be used to guide survey item development/adaptation in this emerging field of inquiry. Key messages • Participants indicated a preference for multi-item questions in relation to climate emotions. • Focusing on coping items was deemed a priority.

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)0.014
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.510
GPT teacher head0.468
Teacher spread0.042 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes2
Has abstractyes

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