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Record W2323678963 · doi:10.1037/a0030412

How dynamic are exercise group dynamics? Examining changes in cohesion within class-based exercise programs.

2012· article· en· W2323678963 on OpenAlexafffund
William L. Dunlop, Carl F. Falk, Mark R. Beauchamp

Bibliographic record

VenueHealth Psychology · 2012
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BC
KeywordsCohesion (chemistry)Group cohesivenessDynamismSalience (neuroscience)PsychologySocial psychologyPsychological interventionSalientDevelopmental psychologyCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Within exercise class settings, group cohesion has consistently been found to predict adherence behaviors, and has been identified as a salient target for intervention-based initiatives. Drawing upon theorizing from the field of group dynamics, exercise class cohesion is often conceptualized as a dynamic construct that requires several classes to form and once it is formed, continues to change over time. Despite the salience of this "dynamic" contention for informing physical activity interventions, this theorizing has yet to be empirically tested. METHOD: In this study a multilevel modeling framework was used to examine changes in exercise class cohesion over time. Exercisers (N = 395) completed measures of cohesion following the second, fifth, and eighth classes of their respective programs (N = 46). RESULTS: Mean levels of social cohesion changed significantly over time whereas mean levels of task cohesion did not. These patterns were largely consistent across persons and groups. CONCLUSIONS: These findings suggest that within group-based exercise programs social and task cohesion possesses different levels of dynamism, and that this dynamism (or lack thereof) might have important implications for future research and interventions involving physical activity 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.097
GPT teacher head0.396
Teacher spread0.299 · 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.

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

Citations31
Published2012
Admission routes2
Has abstractyes

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