Health journal coverage of climate change and health: a bibliometric study
Bibliographic record
Abstract
ABSTRACT Objectives To find what proportion of a broad set of health journals have published on climate change and health, how many articles they have published, and when they first published on the subject. Design Bibliometric study. Setting and particbipants We conducted electronic searches in Ovid MEDLINE ALL for articles about climate change and human health published from 1860 to 31 December 2022 in 330 health journals. There were no limits by language or publication type. Results were independently screened by two raters for article eligibility. Results After screening there were 2932 eligible articles published across 253 of the 330 journals between 1947 and 2022; most (2795/2932; 95%) were published in English. A few journals published articles in the early 90s, but there has been a rapid increase since about 2006. We were unable to categorise the types of publication but estimate that fewer than half are research papers. While articles were published in journals in 39 countries, two-thirds (1929/2932; 66%) were published in a journal published in the UK or the US. Almost a quarter (77/330; 23%) of the journals published no eligible articles, and almost three-quarters (241/330; 73%) published five articles or fewer. The publication of joint editorials in over 200 journals in 2021 and 2022 boosted the number of journals publishing something on climate change and health. A third of the (112/330; 34%) journals in our sample published at least one of the joint editorials, and almost a third of those (32/112; 29%) were publishing on climate change and health for the first time. Conclusions Health journals are rapidly increasing the amount they publish on climate change and health, but despite climate change being the major threat to global health many journals had until recently published little or nothing. A joint editorial published in multiple journals increased coverage, and for many journals it was the first thing they published on climate change and health. Strengths and limitations of this study We looked at coverage of climate change at both the article and journal level from 1860 to 2022; previous bibliometric studies have analysed results only at the article level and in restricted time periods Two independent raters screened the title and abstract (where available) of every article identified by the MEDLINE search to assess eligibility Journals not currently indexed in MEDLINE had to be excluded as hand searching was not feasible for such a large sample of journals It’s important to be cautious in generalising to all health journals from our sample Our study included analysis of an intervention (publication of the joint editorials in multiple journals) to increase coverage on climate change and health in health journals.
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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 | 0.021 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.137 | 0.189 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".