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Record W3011400606 · doi:10.17269/s41997-020-00309-2

Social connection as a public health adaptation to extreme heat events

2020· article· en· W3011400606 on OpenAlexaffvenue
Amani Kafeety, Sarah B. Henderson, Amy A. Lubik, Jesse Kancir, Tom Kosatsky, Michael Schwandt

Bibliographic record

VenueCanadian Journal of Public Health · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsFraser HealthUniversity of British ColumbiaBC Centre for Disease Control
Fundersnot available
KeywordsSocial isolationPsychological interventionPublic healthIsolation (microbiology)Political scienceMedicineNursing

Abstract

fetched live from OpenAlex

Climate change is an increasingly important public health issue, reflected in morbidity and mortality outcomes during extreme heat events. At the same time, the harms of social isolation with respect to a wide range of health outcomes are becoming better understood. Given that older adults are at higher risk during hot weather and at higher risk of social isolation, they are among those at highest risk for adverse impacts of extreme heat events. While specific strategies to reduce heat exposure have been described in the literature and promoted in public health practice, these may not be readily available to socially isolated older adults. As such, it is crucial to identify key approaches to address risk due to social isolation in the aging population, and to acknowledge their limitations and barriers. Interventions rooted in social connection, a concept widely applied in interventions for public health and social well-being, should be applied as a tool for adaptation to extreme heat events.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.361
GPT teacher head0.360
Teacher spread0.001 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations62
Published2020
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Public HealthSame topicClimate Change and Health ImpactsFrench-language works237,207