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Record W4403729885 · doi:10.1080/22423982.2024.2419698

Sauna bathing in northern Sweden: results from the MONICA study 2022

2024· article· en· W4403729885 on OpenAlexaff
Åsa Engström, Hans Hägglund, Earric Lee, Maria Wennberg, Stefan Söderberg, Maria Andersson

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersMedicinska fakulteten, Umeå UniversitetUmeå UniversitetNaturvårdsverket
KeywordsBathingGerontologyEnvironmental scienceGeographyMedicineDemographyPsychologyArchaeologySociology

Abstract

fetched live from OpenAlex

Frequent sauna bathing has been associated with a reduced risk of cardiovascular disease and proposed as a mediator for improved health. Therefore, the aim was to describe and compare sauna bathers with non-sauna bathers in northern Sweden based on their demographics, health and life attitudes, and to describe sauna bathers' sauna habits. Questions on sauna bathing habits were included in the questionnaire for the participants in the Northern Sweden MONICA (multinational monitoring of trends and determinants in cardiovascular disease) study, conducted during spring of 2022, inviting adults 25-74 years living in the two northernmost counties of Sweden (Norr- and Västerbotten), randomly selected from the population register. Of the 1180 participants in MONICA 2022, 971 (82%) answered the question about sauna bathing. Of these, 641 (66%) were defined as sauna bathers. Sauna bathers reported less hypertension diagnosis and self-reported pain. They also reported higher levels of happiness and energy, more satisfying sleep patterns, as well as better general and mental health. Sauna bathers were younger, more often men and found to have a healthier life-profile compared to non-sauna bathers. Additionally, the results suggest that the positive effects associated with sauna bathing plateaued from 1-4 times per month.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.258
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.051
GPT teacher head0.445
Teacher spread0.394 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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