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Record W2095707843 · doi:10.53841/bpssepr.2006.2.1.13

Group versus individual approach? A meta-analysis of the effectiveness of interventions to promote physical activity

2006· article· en· W2095707843 on OpenAlexaff
Shauna M. Burke, Albert V. Carron, Mark Eys, Nikos Ntoumanis, Paul A. Estabrooks

Bibliographic record

VenueSport & Exercise Psychology Review · 2006
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsLaurentian UniversityWestern University
Fundersnot available
KeywordsGroup cohesivenessPsychological interventionMeta-analysisPsychologyIntervention (counseling)Social contactApplied psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

The purpose of the study was to conduct a meta-analysis to empirically compare the relative merits of different contexts typically employed in the physical activity intervention literature for five categories of outcomes: adherence, social interaction, quality of life, physiological effectiveness, and functional effectiveness. Four contexts were examined: home-based programmes not involving contact from researchers or health-care professionals, home-based programmes that involved some contact, standard exercise classes, and exercise classes where group-dynamics principles were used to increase cohesiveness (‘true groups’). Standard literature searches produced 44 relevant studies containing 214 effect sizes. Results revealed a common trend across dependent variables; exercising in a true group was superior to exercising in a standard exercise class, which in turn, did not differ from exercising at home with contact. Furthermore, exercising at home with contact was superior to exercising at home without contact. These results have implications for practitioners in terms of the importance of contact and social support in physical activity interventions.

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)0.038
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.070
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.037
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
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 head0.183
GPT teacher head0.450
Teacher spread0.266 · 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.

Study designMeta-analysis
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

Citations285
Published2006
Admission routes1
Has abstractyes

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