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Record W3018499662 · doi:10.1186/s12889-020-08617-5

The interaction of behavioral context and motivational-volitional factors for exercise and sport in adolescence: patterns matter

2020· article· en· W3018499662 on OpenAlexfundno aff
Vanessa Gut, Julia Schmid, Achim Conzelmann

Bibliographic record

VenueBMC Public Health · 2020
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
FundersStiftung Suzanne und Hans Biäsch zur Förderung der Angewandten PsychologieSaskatoon City Hospital Foundation
KeywordsBehavioral patternContext (archaeology)PsychologyVolition (linguistics)Health psychologyClubSocial environmentDevelopmental psychologyMedicinePublic health

Abstract

fetched live from OpenAlex

BACKGROUND: In order to generate more effective interventions to promote exercise and sport in adolescence, a better understanding of the interaction of influencing factors across different levels is needed. In particular, motivation and volition for exercise and sport, as well as the context in which adolescents are doing exercise and sport, have been identified as important factors. Behavioral context refers to both the organizational setting, e.g., doing exercise and sport in a club, and the social setting, e.g., doing exercise and sport with friends. Extending previous research, the present study applies a person-oriented approach and aims to identify typical behavioral context patterns and motivational-volitional patterns. To validate the patterns, it was examined whether they differ concerning the exercise and sport activity level. Furthermore, the study investigated how behavioral context patterns and motivational-volitional patterns interact. METHOD: = 15.29; 53% female) was applied. A latent profile analysis was used twice to identify typical patterns: once with eight organizational and social setting factors to examine behavioral context patterns, and once with five motivational-volitional factors to examine motivational-volitional patterns. To validate the patterns identified, the exercise and sport activity level were compared across the patterns using Wald-tests. Finally, transition probabilities and odds ratios were calculated in order to investigate the interaction of the behavioral context and motivational-volitional patterns. RESULTS: Four behavioral context patterns - differing in activity level - were identified: Mostly inactive, non-club-organized individualists, self-organized individualists and family sportspersons, and traditional competitive club athletes with friends. Furthermore, five motivational-volitional patterns emerged with differing activity levels: three level patterns with overall low, moderate or high motivation and volition, and two shape patterns called the intention- and plan-less and the plan-less motivated. Regarding interaction, the results indicate that one behavioral context pattern is not solely responsible for moderate to high motivation and volition in adolescents. CONCLUSION: Applying a person-oriented approach allows a more differentiated view of how behavioral context and motivational-volitional factors interact within homogenous subgroups. This, in turn, provides a basis to design tailored multilevel interventions which account for the interaction of influencing factors across different levels.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.086
GPT teacher head0.364
Teacher spread0.277 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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