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Record W4405713429 · doi:10.29173/hsi473

Western Canada’s 2021 heatwave will happen again: Why we need to better protect older adults

2022· article· en· W4405713429 on OpenAlexfundvenueaboutno aff
Jasmine Mah, Prativa Baral, Kowan T. V. O’Keefe

Bibliographic record

VenueHealth Science Inquiry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersPierre Elliott Trudeau Foundation
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakPolitical scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineHistoryVirologyOutbreakInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

The sweltering heat experienced by Canadians during the 2021 heatwave in western Canada is a stark reminder that climate change is not just some far-off problem. It is already here, and we are already reeling from its impacts. Previously thought to be a once-in-a-millennium event, extreme events like this one could occur with a frequency of once every five to ten years. Compared to the rest of the population, older adults – an increasingly large share of the Canadian population – are more susceptible to heat-related trauma because of impaired thermoregulatory responses from aging and other chronic conditions. The compounded effect of climate change and an increasingly older population will necessitate that we expand the availability of health resources and the capacity of health systems in response to these stressors. Our current health care funding mechanisms, as they stand, do not address either of these problems. This commentary explores how the increasing frequency and intensity of temperature extremes impact older adults at both an individual and health systems level. Climate-related stressors in an aging demographic will require that we redefine health resilience – including a serious conversation about health systems resources – and how we currently operationalize it in the Canadian context.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.058
GPT teacher head0.323
Teacher spread0.265 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes3
Has abstractyes

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