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Record W3209597695 · doi:10.1123/jsep.2020-0300

Self-Compassion and Reactions to a Recalled Exercise Lapse: The Moderating Role of Gender-Role Schemas

2021· article· en· W3209597695 on OpenAlexaff
Alana Signore, Brittany Semenchuk, Shaelyn M. Strachan

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
Fundersnot available
KeywordsPsychologySelf-compassionMasculinityFemininityCompassionRuminationMindfulnessAmotivationDevelopmental psychologyClinical psychologySocial psychologyCognitionIntrinsic motivation

Abstract

fetched live from OpenAlex

Exercise is good for health and well-being, yet people experience lapses when trying to adhere to exercise. Self-compassion may help people cope with exercise lapses. Most research on self-compassion and exercise has been conducted with women; men may also benefit from self-compassion. No research has examined whether gender-role schema influences responses to exercise lapses. The authors examined both male and female adult exercisers (N = 220) who reported their self-compassion, recalled an exercise lapse, their reactions to the lapse, and their self-identification of masculinity and femininity. After controlling for self-esteem, age, and lapse importance, self-compassion negatively related to emotional responses (p < .001), rumination (p < .001), extrinsic motivation (p = .004), and positively related to intrinsic motivation (p < .001). Masculinity moderated the relationships between self-compassion and amotivation (p = .006), and identified regulation (p = .01). Self-compassion may be an effective resource for exercisers, especially those who identify as highly masculine.

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)0.011
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.330
Teacher spread0.302 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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