Canadian adolescents’ perceptions of how climate change is impacting their mental health: A qualitative analysis of open-ended survey responses
Bibliographic record
Abstract
There is increasing recognition that climate change affects mental health, with young people identified as a high-risk population. Yet, research on this topic has mostly focused on quantitative studies with limited research exploring youth's perspectives. This study explored Canadian adolescents' perceptions of the impacts of climate change on their mental health. Data were collected from a cross-sectional survey of 804 Canadian adolescents (13-18 years). This study utilized open-ended response data from participants who reported that their mental health was impacted by climate change and who were then asked to describe how. An inductive thematic analysis with semantic coding was undertaken to analyze the data. Thirty-seven percent of participants reported that they felt climate change was impacting their mental health either a little or a lot, and 235 participants answered the open-ended question about how. In the open-ended responses, four themes emerged: i) Emotional and psychological responses, ii) Concerns for the future, iii) Impacts on functioning, and iv) Concerns for the environment, humanity, and wildlife. This study highlights that adolescents' perspectives of the impacts of climate change on their mental health were diverse, highlighting multiple pathways linking climate change to mental health among this demographic. These insights can inform strategies to mitigate the climate-related mental health impacts among 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 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".