Planetary health for health systems: A scoping review and content analysis of frameworks
Bibliographic record
Abstract
Planetary health movements have advanced substantially within the last ten years with new frameworks and models being considered within health systems in varied contexts. Despite advancements, there continues to be an overall lack of accessible and regional- or field-specific planetary health frameworks to inform health systems. We therefore set out to conduct a scoping review to identify current planetary health-related frameworks that have been developed for health systems. We systematically searched the following electronic databases up to November 2023: Medline, CAB Abstracts, and Scopus; and carried out manual searches in Overton, Policy Commons, Google, and Google Scholar. We engaged a two-stage article review process, then used content analysis to identify the different domains. We identified six overarching categories within the planetary health-related frameworks including: 1) health system and environmental impacts; 2) vision, advocacy, leadership, and communication elements; 3) key structural components for environmentally sustainable health systems; 4) climate resiliency and environmental sustainability of healthcare facilities and systems; 5) climate-resilient and sustainable technologies and infrastructure; and 6) evaluation and accountability mechanisms. Regional, national, and international governments, funding agencies, and organizations are called to support greater research and implementation work around planetary health-informed health systems change while considering existing frameworks. Better inclusion of all facets of planetary health (e.g., biodiversity), as well as key acknowledgement and work with other knowledge systems (e.g., Indigenous Peoples and their knowledge systems) are needed to ensure planetary health-related frameworks are grounded in what we are trying to protect-the planet itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.137 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.077 | 0.066 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".