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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".