Mass Media’s Potential to Increase Climate Anxiety, Self-Efficacy, and Pro-Environmental Behaviour: A Research Protocol
Bibliographic record
Abstract
Introduction: As the frequency of hazardous weather events increases due to climate change, mental health around the world is impacted both directly and indirectly. Specifically, young adults are experiencing an increase in climate anxiety. Typically, people gain information on climate change from mass media which may have the potential to influence levels of climate anxiety as well as self-efficacy beliefs. To better understand this relation, this study will examine whether mass media can increase climate anxiety and self-efficacy to increase engagement in pro-environmental behaviour. Methods: To test this, university students will be invited to participate in a study on climate change and will be randomized to one of three conditions. In the first condition, students will read a science-based news article with a call-to-action. In the second condition, students will read a science-based news article without a call-to-action. In both conditions, the students will fill out items measuring climate anxiety, self-efficacy, and pro-environmental behaviour after reading the article. In the third condition (i.e., the control), students will fill out the same items without reading an article. Two weeks after completing the survey, participants will be contacted to confirm whether they engaged in pro-environmental behaviour. Results: It is expected that students in the science-based call-to-action condition will experience an increase in climate anxiety, self-efficacy, and pro-environmental behaviour. Similarly, those in the science-based no call-to-action group are predicted to report an increase in climate anxiety after reading the article. However, they are also predicted to experience a decrease in self-efficacy and pro-environmental behaviour. The control condition will not experience an increase in any of these measures. Discussion: The findings of this study will clarify what factors in the media encourage adaptive responses to climate anxiety. Specifically, this study will examine whether media can increase self-efficacy, thereby increasing pro-environmental behaviours. Conclusion: Overall, this research will provide insight into best practices for encouraging pro-environmental behaviour among young adults, who seem to be the most vulnerable to climate anxiety and will support the mitigation of climate change.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".