MétaCan
Menu
Back to cohort
Record W4405941597 · doi:10.12968/bjcn.2024.0003

The impact of cold weather on older people and the vital role of community nurses

2024· article· en· W4405941597 on OpenAlexaboutno aff
Tiago Manuel Horta Reis da Silva

Bibliographic record

VenueBritish Journal of Community Nursing · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionQuarter (Canadian coin)Older peopleGerontologyCold climateExtreme weatherNursingClimate changeGeography

Abstract

fetched live from OpenAlex

Over the past 160 years, the UK has experienced significant winter mortality among older people. A quarter of a million older people have died from cold-related illnesses over the past decade, with one older person dying every seven minutes. Misconceptions about winter deaths must be addressed. Evidence shows that winter deaths are avoidable. These deaths are not because of hypothermia and are unlikely to decline with climate change in the future. Improving indoor heating may only partially reduce winter deaths. An integrated policy is needed to reduce all risks equally, with community nursing playing a crucial role in such policies. This article explores the multifaceted impact of cold weather on older individuals and emphasises the crucial role that community nurses play in mitigating the associated challenges. Grounded in advanced concepts and research methodologies, the analysis encompasses physiological, psychological and social dimensions. The integration of primary sources and academic theories aims to provide a comprehensive understanding of the topic. The article also explores the specific responsibilities of community nurses and the evidence-based interventions required to address the unique needs of older individuals during the colder seasons.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.335
Teacher spread0.307 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Community NursingSame topicClimate Change and Health ImpactsFrench-language works237,207