Impact of awareness and concerns of climate change on childrenʼs mental health
Bibliographic record
Abstract
OBJECTIVE: The purpose of this scoping review is to identify and describe the existing literature on the impact of the overarching awareness and concerns of climate change on children's mental health and well-being. INTRODUCTION: Children are widely acknowledged as being disproportionately at risk to the effects of climate change, yet research overlooks the impact that climate change has on their mental health. Children's overarching awareness of climate change, and its global effects, may influence their mental health and well-being. INCLUSION CRITERIA: This review will include all research that addresses school-aged children's (aged 3-19) mental-health issues stemming from an awareness of climate change. It will not include research that examines direct impacts of climate change on children's mental health, such as trauma from a specific climate-related event. METHODS: Searches will be conducted across eight research databases (Cochrane Database of Systematic Reviews, CINAHL, Embase, GreenFILE, PubMed, PsycINFO, Web of Science, and Scopus) and three unpublished/gray literature databases (ProQuest Dissertations and Theses, GreyLit.org, and OpenGrey). Data will be extracted for author(s), year of publication, country of origin, purpose, population, methodology, concepts of interest, outcomes, and key findings relating to the scoping review objectives. Findings will be presented as a narrative summary.
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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".