A call for mixed-methods research on climate change, family systems and child mental health
Bibliographic record
Abstract
Climate change is reshaping the contexts in which children grow and develop, with implications for their mental health and the functioning of their families. Despite increasing concern for children, this area remains underexplored in empirical research. Accordingly, this viewpoint calls for mixed-methods research, using diverse and robust methodologies, to explore how climate threats affect child mental health through a family-centred lens. We emphasise the utility of considering ecological and family systems models in this endeavour. Throughout this article, mental health can be understood as “…a state of well-being in which an individual realises (their) own abilities, can cope with the normal stresses of life, can work productively and is able to make a contribution to (their) community.’1
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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 | 0.697 | 0.706 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".