Scoping Review and Bibliometric Analysis of the Term “Planetary Health” in the Peer-Reviewed Literature
Bibliographic record
Abstract
Background: Planetary Health is an emerging holistic health field to foster interdisciplinary collaborations, integrate Indigenous knowledge, facilitate education, and drive public and policy engagement. To understand to what extent the field has successfully met these goals, we conducted a scoping review and bibliometric analysis. Methods: We searched 15 databases from 2005 to 2019 for peer-reviewed publications with the term ‘planetary health’, in the title, abstract and/or keywords, with no language or geographical location limitations. We classified results into four categories (commentaries, comprehensive syntheses, educational material, original research) and categorized original research according to expert-derived planetary health themes. Our bibliometric analysis highlighted publications over time, collaborations, and networks of keywords. Findings: Only 8.1% (n=22) were research articles. Publications rose rapidly from eight to 64 publications per year in 2015-2018. The top five author affiliation countries for most publications were the US, UK, Australia, Canada, and New Zealand, and the top five collaborations were a subset of pairwise combinations between the US, UK, Australia, and Canada. The most common author keywords were: planetary health, climate change, ecology, and non-communicable diseases. Keyword co-occurrences clustered around high-level concepts (e.g., Anthropocene) and food system-related topics; two clusters lacked a theme. Interpretation: We show that the term planetary health is used mainly in commentary-like publications, not original research. Additionally, more global collaborations are lacking. Interdisciplinary work, as represented by keyword co-occurrence networks, is developing but could potentially be extended. The planetary health community should promote more worldwide research and interdisciplinary collaborations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.108 |
| 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".