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Record W3047881349 · doi:10.29173/hsi287

Mitigating the effects of the climate crisis through health policy

2020· article· en· W3047881349 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Science Inquiry · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsClimate changeVulnerability (computing)BusinessNatural resource economicsHealth careEnvironmental planningEnvironmental resource managementEnvironmental healthEconomic growthGeographyMedicineEnvironmental scienceEconomicsEcology

Abstract

fetched live from OpenAlex

In Canada, the climate crisis has had profound impacts on health, including increased health problems as well as decreased access to healthcare services. Without mitigation, the climate crisis is expected to exacerbate an abundance of negative health outcomes and health system disruptions in Canada, including: food, water, and shelter insecurity; increased health problems caused by severe weather; and forced displacement from geographically vulnerable areas. Governmental action could be taken to mitigate the effects of the climate crisis and improve healthcare in the country. This report synthesizes current literature on how the climate crisis is affecting health in Canada. It also recommends 3 actions that can be taken to mitigate the effects of the climate crisis and improve the country’s health. Actions include: conducting climate change and health vulnerability assessments (CCHVAs), enhancing support for climate action research, and reducing CO2 emissions in the healthcare sector.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.111
GPT teacher head0.408
Teacher spread0.297 · 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