Climate change, migration and health systems resilience: Need for interdisciplinary research
Bibliographic record
Abstract
<ns4:p>Climate change is one of today's major challenges, among the causes of population movements and international migration. Climate migrants impact health systems and how they respond and adapt to their needs and patterns. But to date, the resilience of health systems in the context of climate change has been little explored.</ns4:p> <ns4:p>The purpose of this article is to show the importance of studying, from an interdisciplinary perspective, the relationships between climate change, migration, and the resilience of health systems.</ns4:p> <ns4:p>Resilience is an old concept, notably in the field of psychology, and is increasingly applied to the study of health systems. Yet, no research has analysed the resilience of health systems in the context of climate change. While universal health coverage is a major international goal, little research has to date focused on the existing links between climate, migration, health systems and resilience.</ns4:p> <ns4:p>We propose an interdisciplinary approach relying on the concept of health system resilience to study adaptive and transformative strategies to articulate climate change, migration and health systems.</ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".