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Record W2737974920

Women's self-compassion and exercise motivations

2015· article· en· W2737974920 on OpenAlexaff
Chelsey Nicole Moore, Ridhi Gupta, Margo E Killham, Kent C. Kowalski

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2015
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSelf-compassionPsychologyMultilevel modelScale (ratio)Clinical psychologyCompassionComputer-assisted web interviewingMindfulness
DOInot available

Abstract

fetched live from OpenAlex

To gain a further understanding of the role of self-compassion in women's exercise motivations the purpose of this research was to examine the relationship between self-compassion and women's exercise motivations. Participants were 72 women exercisers between 18 and 40 years (M = 24.18, SD = 5.83). The women completed an online questionnaire that included a demographic survey, the Behavioural Regulation in Exercise Questionnaire, the Rosenberg Self-Esteem Scale, the Self-Compassion Scale, the Self- determination Scale, the modified Drive for Muscularity Attitudes Questionnaire, the Drive for Thinness Scale, and the Body Appreciation Scale. The results showed that self-compassion was negatively correlated with introjected motivation (r= -.50, p <.05), drive for muscularity (r= -.30, p <.05), and drive for thinness (r = -.54, p <.05); and that self-compassion was positively correlated with body appreciation (r= .73, p <.05) and self-determined motives to exercise (r= .53, p <.05). Follow-up hierarchical regression analyses showed that self-compassion predicted unique variance beyond self-esteem on introjected motivation (R2 = .25, p <.05; ?R2 = .06, p <.05), drive for thinness (R2 = .29, p <.05; ?R2 = .05, p <.05), and self-determined motives to exercise (R2 = .28, p <.05; ?R2 = .05, p <.05). The current study highlights that self-compassion might play a role in the promotion of adaptive, healthy, and positive exercise motivations for women exercisers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.028
GPT teacher head0.297
Teacher spread0.269 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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