Climate change, mental health, and reproductive decision-making: A systematic review
Bibliographic record
Abstract
The impact of climate change on reproductive decision-making is becoming a significant issue, with anecdotal evidence indicating a growing number of people factoring their concerns about climate change into their childbearing plans. Although empirical research has explored climate change and its relationship to mental health, as well as the motivations behind reproductive decision-making independently, a gap in the literature remains that bridges these topics at their nexus. This review endeavours to fill this gap by synthesising the available evidence connecting climate change-related concerns with reproductive decision-making and exploring the reasons and motivations behind this relationship. A systematic review using six databases was conducted to identify relevant literature. Included studies reported quantitative, qualitative, and mixed-methods data related to: (1) climate change, (2) mental health and wellbeing concerns, and (3) reproductive decision-making. Findings were synthesised narratively using a parallel-results convergent synthesis design and the quality of studies was appraised using three validated assessment tools. Four hundred and forty-six documents were screened using pre-defined inclusion criteria, resulting in the inclusion of thirteen studies. The studies were conducted between 2012 and 2022 primarily in Global North countries (e.g., USA, Canada, New Zealand, and European countries). Climate change concerns were typically associated with less positive attitudes towards reproduction and a desire and/or intent for fewer children or none at all. Four themes explaining this relationship were identified: uncertainty about the future of an unborn child, environmentalist views centred on overpopulation and overconsumption, meeting family subsistence needs, and environmental and political sentiments. The current evidence reveals a complex relationship between climate change concerns and reproductive decision-making, grounded in ethical, environmental, livelihood, and political considerations. Further research is required to better understand and address this issue with an intercultural approach, particularly among many highly affected Global South populations, to ensure comparability and generalisable results.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".