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Record W4395694898 · doi:10.21203/rs.3.rs-4298695/v1

Increasing ambient temperature disrupts sleep and impairs cognitive function among Older Adults

2024· preprint· en· W4395694898 on OpenAlexaff
Godfred O. Boateng, Gabriel John Dusing, Shaira Shafiquzzaman, Stella T. Lartey, Eyram Agbe, Reginald Quansah, Dozie Okoye

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsYork UniversityDalhousie UniversityWestern University
Fundersnot available
KeywordsSleep (system call)CognitionGerontologyPsychologyFunction (biology)AudiologyMedicineNeuroscienceComputer scienceBiologyCell biology

Abstract

fetched live from OpenAlex

Abstract Increases in ambient temperature have been associated with deleterious consequences for socially disadvantaged populations. However, there is limited research on the associated mental health effects. Thus, this study examined the direct and indirect effects of high temperatures on sleep quality and cognitive function among older adults. Using combined data from the WHO Ghana Study on Global Ageing and Adult Health with temperature measurements derived from the Climatic Research Unit gridded Time Series and structural equation models, we examined the direct and indirect relationship between increasing mean temperatures, sleep difficulties, and cognitive impairment while adjusting for appropriate covariates. Increases in mean temperature change were associated with an increase in the severity of sleep difficulties and a decrease in cognitive function. The effect of temperature significantly increased sleep difficulties for females and older adults (65+). Our study shows that a reduction in temperature will improve sleep quality and enhance cognitive function.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.372
Teacher spread0.329 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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