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Record W2783121892

Mission accomplished? Physical activity, affect, and self-esteem

2015· article· en· W2783121892 on OpenAlexaffabout
Andrea Bedard, Melanie Gregg

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsAffect (linguistics)Physical activitySelf-esteemPsychologyClinical psychologySocial psychologyPhysical therapyMedicine
DOInot available

Abstract

fetched live from OpenAlex

The homeless experience a variety of physical, psychological and social vulnerabilities; physical activity and sport may be one vehicle to help alleviate some of these challenges. Self-esteem can predict intention and moderate the level of control the individual perceives they have over a specific health behaviour such as physical activity. There is evidence that even a single bout of physical activity can positively change affective states. Patrons from a homeless shelter (N = 34) participated in one of four physical activity experiences. Four physical activity experiences were selected based on results from our previous research: bowling, yoga, war canoe and Atlatl, and ball hockey. Self-report exercise behaviour and intentions were assessed prior to activity participation. Self-esteem and physical activity affect were measured before and after each activity. There were no significant changes in self-esteem or affect following physical activity participation. No between group differences were evident for self-esteem or affect. Self-esteem was not a significant predictor of self-reported exercise behaviour. Exercise behaviour was a significant predictor of physical activity affect; F(1, 27) = 5.74, p = .02; with an R2 = .18. Participants who participated more regularly in physical activity had higher ratings of physical activity affect. Acknowledgments: University of Winnipeg Major Research Grant

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.468
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.391
Teacher spread0.327 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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