What is the scope and nature of the existing literature on the contribution to global warming of hospitals and clinical services?
Bibliographic record
Abstract
There is evidence that climate change represents one of the greatest threats to public health today, increasing infection and respiratory disease, increased heat-related mortality, increased food-borne disease, and increased risk of vector-borne and water-borne disease, exerting pressure on the health sector, with resources always limited(1). The hospital facilities that care for people themselves contribute to greenhouse gases emissions. The healthcare activity requiring therapeutics resources, expensive equipment, sterilization procedures, advanced operative technologies, and obligatory life support systems. All of them are sources of emissions, with a high-level energy consumption (2). The health sector, in the USA, generates between 8 and 10% of total GHG production and are responsible for the loss of up to 470,000 disability-adjusted life years (DALYs) annually (405,000 DALYs when adjusted for reductions in the carbon intensity of electricity generation)(3) and Canada’s healthcare system was responsible for 33million tons of carbon dioxide equivalents annually (2). The Australian healthcare sector accounts for 7% of total national emissions (4). Thus, there is a growing interest among researchers, universities, organizations and governments to study the impact of the health sector on the environment and the development of strategies to mitigate it (5). The Declaration of Helsinki to protect human and planetary health by the year 2020 emphasizes the urgency of action (6). There by, there is a wide variety of publications with different levels of evidence describing therapeutic procedures, hospitalizations in ICU, surgeries, use of devices, among others, that study the relationship between health sector activities and their environmental impact. (7,8). In general, Scoping Reviews are commonly used to clarify the limits of what has been published so far and are especially useful when a body of literature has not yet been comprehensively reviewed, or is of a large, complex or heterogeneous nature that is not amenable to a more precise systematic review (9). The scoping reviews can examine the extent (i.e., size), range (variety), and nature (characteristics) of the evidence on a topic or question, summarize the findings of a body of knowledge that is heterogeneous in terms of methods and identify gaps in the literature (10). Therefore, through a scoping review, we hope to respond to our interest in knowing the extent and nature of the existing literature on the contribution to global warming from the activities of hospitals and clinical services, identifying areas of knowledge in which there are gaps will allow us to work on the planning and implementation of future research and contribute to the imperative of hospital sustainability.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".