Climate and health education in public health schools worldwide during 2023–24: a survey
Bibliographic record
Abstract
BACKGROUND: Public health professionals are crucial in implementing health-promoting climate change adaptation and mitigation measures, yet climate education is inconsistently integrated into public health curricula worldwide. We aimed to assess the proportion of institutions that provided public health degrees with climate and health education, the annual number of students trained in climate and health, and the extent to which students had climate and health knowledge during 2023-24. METHODS: From Nov 1, 2023, to March 15, 2024, our online survey quantified climate and health education in public health schools that provide degrees across all WHO regions. The survey was available in English, Spanish, and Portuguese and distributed to Global Consortium on Climate and Health Education member institutions and organisations and the Global Network for Academic Public Health; institutions in 138 countries were invited to participate. We collected data on optional and mandatory training, enrolment in versus actual education on climate topics, degree programmes offering climate education, year of curriculum implementation, and the extent of training across eight competency domains. Instructions stated that the survey should be completed by school staff who designed, taught, or were familiar with climate or planetary health content and curricula within their institution. Two follow-up reminder emails were sent to institutions that had not completed the survey on Jan 13, 2024, and Feb 15, 2024. We also measured the presence of climate education among randomly selected non-responding institutions through internet searches for evidence of a class or a concentration from June 1 to July 25, 2024. FINDINGS: The survey was sent to 1251 public health institutions across 138 countries; we received responses from 279 (22%) of 1251 institutions in 81 (59%) of 138 countries. Most institutions that we invited were in the WHO region of the Americas (n=776), the African region (n=177), and the European region (n=155). 196 (70%) of 279 responding institutions and 62 (77%) of 81 responding countries reported providing climate and health education during 2023-24. The number of responding institutions providing climate and health education was 53 (80%) of 66 in the European region, 21 (72%) of 29 in the Western Pacific region, five (71%) of seven in the South-East Asia region, 97 (68%) of 143 in the region of the Americas, 15 (63%) of 24 in the African region, and five (50%) of ten in the Eastern Mediterranean region. 298 degree-level public health programmes were identified during 2023-24, of which 171 (57%) reported that climate and health education was part of the required curriculum. Master's degree programmes provided the most climate and health education (118 [40%] of 298 degree-level programmes identified). A search of 135 additional non-responding institutions indicated that 36 (27%) likely offered climate and health education. INTERPRETATION: Our global survey of institutions that provide public health degrees found widespread integration of climate topics in public health curricula. To address disparities, future work should prioritise integrating climate and health education globally, increasing investments, and securing institutional and political support. International cooperation and national engagement are essential to achieve comprehensive climate education across all public health training programmes. FUNDING: None.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".