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Record W2145049178 · doi:10.1177/1049732305286051

Factors That Influence Physical Activity Participation Among High- and Low-SES Youth

2006· article· en· W2145049178 on OpenAlexaff
M. Louise Humbert, Karen Chad, Kevin S. Spink, Nazeem Muhajarine, Kristal D. Anderson, Mark W. Bruner, Tammy M. Girolami, Patrick Odnokon, Catherine R. Gryba

Bibliographic record

VenueQualitative Health Research · 2006
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsSaskatoon City HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsIntrapersonal communicationPhysical activityPsychologySocioeconomic statusPerspective (graphical)Positive Youth DevelopmentYouth participationCompetence (human resources)Developmental psychologyFocus groupGerontologySocial psychologySociologyInterpersonal communicationMedicineDemographyPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

Researchers have rarely addressed the relationship between socioeconomic status (SES) and physical activity from the perspective of youth. To illuminate the factors that youth from low and high-SES areas consider important to increase physical activity participation among their peers, 160 youth (12-18 years) participated in small focus group interviews. Guiding questions centered on the general theme, "If you were the one in charge of increasing the physical activity levels of kids your age, what would you do?" Findings show that environmental factors (i.e., proximity, cost, facilities, and safety) are very important for youth living in low-SES areas to ensure participation in physical activity. Results also show that intrapersonal (i.e., perceived skill, competence, time) and social factors (i.e., friends, adult support) must be considered to help improve participation rates among both high- and low-SES youth.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.543
GPT teacher head0.596
Teacher spread0.054 · 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 designQualitative
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

Citations310
Published2006
Admission routes1
Has abstractyes

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