Planetary health curriculum in higher education: Scoping review
Bibliographic record
Abstract
Introduction: Human actions on this planet have caused numerous changes and negative impacts on society and the ecosystem. Thus, the field of Planetary Health emerged with the need to adopt approaches that reconcile natural and human systems. However, this field of study is still little recognized in the internal and external environments of the scientific community and society in general, especially in developing countries. Objective: The objective is to provide a comprehensive view of publications on the actions and scenarios of higher education regarding planetary health that can help in the formulation of teaching plans on planetary health. This article aims to review and describe variables such as curriculum, workload, and main methods and approaches used in higher education on planetary health. Methods: This is a scoping review based on the evaluation of studies that address teaching methods in undergraduate health courses. The study was conducted in July 2022, in the electronic databases, Scopus, PubMed, and EMBASE, by the identification, selection, mapping, and summary of the included studies were carried out. Results: The articles included indicate that there are few reports of experiences in countries of the Global South and such curricula are especially focused on medicine and nursing courses. Conclusion: Current and future health professionals are not yet prepared for the challenges they will face regarding planetary health. Thus, the need to include this theme in curricula is urgent and should be considered a priority in new curriculum proposals.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".