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Record W1981985902 · doi:10.1080/1612197x.2010.9671952

Group goal setting and group performance in a physical activity context

2010· article· en· W1981985902 on OpenAlexaff
Shauna M. Burke, Kim M. Shapcott, Albert V. Carron, Michael H. Bradshaw, Paul A. Estabrooks

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyModerationGroup cohesivenessPhysical activityContext (archaeology)Self-efficacyGroup (periodic table)Developmental psychologySocial psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

The primary purpose was to examine the relationship between group goal setting and group performance in an exercise setting. The secondary purpose was to determine whether cohesion, self‐efficacy, and physical activity level influenced the magnitude of the group‐goal/group‐performance relationship. The sample consisted of 6,356 participants (N = 1,325 groups) who were registered for an 8‐week walking program. Results revealed a positive and significant relationship between group goal setting and group performance. Analyses also showed that cohesion was not a moderator while physical activity level and self‐efficacy were; the strength of the relationship between group goal setting and group performance was enhanced as the group average for self‐efficacy and physical activity increased. Further analyses revealed that physical activity level and self‐efficacy interacted in a conjunctive manner to influence the group‐goal/group‐performance relationship; groups high in physical activity and self‐efficacy showed a stronger relationship than groups with other combinations of the two

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)0.002
metaresearch head score (Gemma)0.010
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.388
Teacher spread0.363 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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