Assessing the role of education level on climate change belief, concern and action: a multinational survey of healthcare professionals in nephrology
Bibliographic record
Abstract
BACKGROUND: Climate change poses a significant risk to kidney health, and countries with lower national wealth are more vulnerable. Yet, citizens from lower-income countries demonstrate less concern for climate change than those from higher-income countries. Education is a key covariate. To examine its role in explaining this perception gap, we obtained the perspectives of a highly educated cohort of healthcare professionals. METHODS: This was a cross-sectional survey of healthcare professionals involved in kidney care. Responses were compared by the income level of the participant's country (per World Bank). RESULTS: Of the 849 healthcare professionals from 107 countries (63.4% from lower and middle-income countries) that participated, most believed climate change was happening (97.9%), displayed a high level of concern (73.3%), and took personal action to combat climate change (62.0%). While the proportion who believed in climate change did not vary by income level (high:98.1%, upper-middle:97.2%, lower-middle:97.8%, low:100%, p = 0.73), the proportion with a higher level of concern (high:80.7%, upper-middle:74.9%, lower-middle:67.5%, low:53.8%, p < 0.001), and who took climate action (high:76.2%, upper-middle:63.1%, lower-middle:51.2%, low:30.8%, p < 0.001) decreased by national wealth. Barriers to involvement in sustainable kidney care were lack of time (54.4%), knowledge (39.7%), and peer support (30.3%). Only 34.0% were aware of national mitigation plans and barriers related to finances, technologies, tools, methods, research, and evidence were perceived as greater obstacles in lower-income countries. CONCLUSIONS: Our results highlight that predictors and correlates of climate change risk perception vary across countries. Education alone is unlikely to increase individual and group engagement in climate change. A better understanding of these factors can inform strategies towards climate action in different settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".