Climate change and its implications for developing brains – In utero to youth: A scoping review
Bibliographic record
Abstract
The brain health and development implications of climate change are situated within a large and rapidly increasing body of evidence that addresses the physical and mental health impacts and implications of extreme and worsening environments. The costs to individuals and societies of negatively impacted brain development are profound – be it in the form of diagnosable developmental disability, reduced cognitive capacity, or areas of behavioral functioning. We have sought to describe the key risk domains that climate change presents with respect to healthy brain development, from the prenatal through to youth stages. Scoping review methods and an a priori search strategy were used to address the question: What are the major considerations of the peer-reviewed literature that address climate change as it relates to brain development and health from early development through to youth populations? Themes from the identified papers were charted, and findings were summarized through a consensus process. A total of 40 papers were identified in the search, spanning 2008-2022. Based on the thematic analysis, results are organized into the following nine themes: 1) heat extremes, 2) weather extremes and stress, 3) air pollution, 4) vector and waterborne illnesses, 5) malnutrition, 6) equity, 7) economic implications, 8) methods issues, and 9) responses. There is a clear consensus amongst the papers in this review suggesting that changing climate patterns and weather extremes have substantial and wide-ranging effects on developing brains. A range of responses are proposed with emphasis upon early intervention and better data.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".