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Record W4411870118 · doi:10.1038/s41598-025-05043-8

Social and demographic determinants of Nordic Walking practice models

2025· article· en· W4411870118 on OpenAlexaboutno aff
Wioletta Szymczak, Krzysztof Jurek, Monika Dobrogowska

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsData scienceGeographyDemographyComputer scienceSociology

Abstract

fetched live from OpenAlex

The article is devoted to the sociological exploration of the social phenomenon that Nordic Walking has become in Europe and worldwide over the recent decades. The purpose of the study was to develop models of Nordic walking, relating to the motivating factors, dominant elements and functions of this activity. In addition, the socio-demographic determinants of the developed models are indicated. The study included 416 Poles, 132 Europeans from 11 countries (Germany, the UK, Spain, France, Italy, Portugal, Ireland, Denmark, Austria, Sweden, and Norway), and 212 participants from 5 non-European countries (the USA, Canada, Australia, New Zealand, and Japan). We have distinguished four models of Nordic Walking, relating to selected core elements in the analysis of social action and social participation: personal development and self-realization; health and fitness; satisfaction and well-being; ties and community. Each of them is a combination of motives, key elements considered by respondents as dominant in the experience of practicing Nordic Walking, and the functions the activity performs. At the same time, each of the models is constituted by a specific set of factors showing what socio-demographic characteristics are associated with the orientation to particular arrangements of leading elements. Our survey provides valuable information on the areas where the content and functions of the sport are located. The knowledge obtained can form the basis for designing health policies and preventive measures. It can also be useful for animators and organizers of local initiatives when creating health, activation or integration offers tailored to the needs of different age groups.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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
Published2025
Admission routes1
Has abstractyes

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