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Record W2036472176 · doi:10.1007/s00038-012-0422-5

Health co-benefits and risks of public health adaptation strategies to climate change: a review of current literature

2012· review· en· W2036472176 on OpenAlexaff
June J. Cheng, Peter Berry

Bibliographic record

VenueInternational Journal of Public Health · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsHealth CanadaMcMaster University
Fundersnot available
KeywordsPublic healthMental healthClimate changeEnvironmental healthSocial determinants of healthAdaptation (eye)Public economicsPsychologyEnvironmental resource managementMedicineEnvironmental planningBusinessGeographyEconomicsPsychiatryNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Many public health adaptation strategies have been identified in response to climate change. This report reviews current literature on health co-benefits and risks of these strategies to gain a better understanding of how they may affect health. METHODS: A literature review was conducted electronically using English language literature from January 2000 to March 2012. Of 812 articles identified, 22 peer-reviewed articles that directly addressed health co-benefits or risks of adaptation were included in the review. RESULTS: The co-benefits and risks identified in the literature most commonly relate to improvements in health associated with adaptation actions that affect social capital and urban design. Health co-benefits of improvements in social capital have positive influences on mental health, independently of other determinants. Risks included reinforcing existing misconceptions regarding health. Health co-benefits of urban design strategies included reduced obesity, cardiovascular disease and improved mental health through increased physical activity, cooling spaces (e.g., shaded areas), and social connectivity. Risks included pollen allergies with increased urban green space, and adverse health effects from heat events through the use of air conditioning. CONCLUSIONS: Due to the current limited understanding of the full impacts of the wide range of existing climate change adaptation strategies, further research should focus on both unintended positive and negative consequences of public health adaptation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.635
GPT teacher head0.539
Teacher spread0.095 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations149
Published2012
Admission routes1
Has abstractyes

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