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Record W2809155394 · doi:10.5430/jnep.v8n11p30

Self-evaluation of lifestyle and assessment of health condition by clinical measurements – A call to the rural population

2018· article· en· W2809155394 on OpenAlexvenueno aff
Pirjo Kaakinen, Uroš Železnik, Helvi Kyngäs, Danica Železnik

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontologyRural areaQuality of life (healthcare)Rural populationPhysical activityElderly peoplePhysical therapyPopulationEnvironmental healthDemographyNursing

Abstract

fetched live from OpenAlex

Background: Although many people know that a healthy lifestyle prevents chronic diseases and improves the quality of life, the best way to invite people from rural areas to take part in health check-ups is still unclear. The aim of this study was to examine the lifestyle and health condition of people from the Carinthia regions in Slovenia.Methods: A cross-sectional study was conducted on 140 participants. Data were collected by questionnaire and clinical measurements and were analysed by descriptive statistical methods.Results: Most of the participants were ≥ 60 years old (62%) and 61% were women, 75% had a high BMI and 64% had elevated blood pressure. The older participants ate breakfast more often than younger participants (p = .010). There was a statistically significant connection between the number of daily hot meals and BMI (p = .026) and blood pressure (p = .049). Half of the participants (51%) drank a litre of water per day as recommended. Hiking was the most popular form of physical activity.Conclusions: The study findings recommended using a new way to call people in health check-ups in rural areas and provided information about the kind of lifestyle counselling rural people may need.

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)0.023
metaresearch head score (Gemma)0.002
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 score0.973
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.002
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 science0.0000.000
Research integrity0.0000.000
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 head0.250
GPT teacher head0.590
Teacher spread0.341 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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