Measuring negative emotional responses to climate change among young people in survey research: A systematic review
Bibliographic record
Abstract
BACKGROUND: Climate change is a threat to the mental and emotional wellbeing of all humans, but young people are particularly vulnerable. Emerging evidence has found that young people's awareness of climate change and the danger it poses to the planet can lead to negative emotions. To increase our understanding about this, survey instruments are needed that measure the negative emotions young people experience about climate change. RESEARCH QUESTIONS: (1) What survey instruments are used to measure negative emotional responses to climate change in young people? (2) Do survey instruments measuring young people's negative emotional responses to climate change have evidence of reliability and validity? (3) What factors are associated with young people's negative emotional responses to climate change? METHODS: A systematic review was conducted by searching seven academic databases on November 30, 2021, with an update on March 31, 2022. The search strategy was structured to capture three elements through various keywords and search terms: (1) negative emotions, (2) climate change, and (3) surveys. RESULTS: A total of 43 manuscripts met the study inclusion criteria. Among the 43 manuscripts, 28% focused specifically on young people, while the other studies included young people in the sample but did not focus exclusively on this population. The number of studies using surveys to examine negative emotional responses to climate change among young people has increased substantially since 2020. Survey instruments that examined worry or concern about climate change were the most common. CONCLUSION: Despite growing interest in climate change emotions among young people, there is a lack of research on the validity of measures of such emotions. Further efforts to develop survey instruments geared to operationalize the emotions that young people are experiencing in relation to climate change are needed.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".