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Record W3159524328 · doi:10.1123/jsep.2020-0196

“Know That You’re Not Just Settling”: Exploring Women Athletes’ Self-Compassion, Sport Performance Perceptions, and Well-Being Around Important Competitive Events

2021· article· en· W3159524328 on OpenAlexaff
Margo E. K. Adam, Abimbola O. Eke, Leah J. Ferguson

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyAthletesSelf-compassionSport psychologyCognitive reframingCompetitive athletesSocial psychologyPerceptionThematic analysisCompassionApplied psychologyMindfulnessPsychotherapistPhysical therapyQualitative research

Abstract

fetched live from OpenAlex

Self-compassion, an adaptive self-attitude, is a resource that women athletes use during emotionally difficult times and as a way to reach their potential. The relationship between self-compassion and sport performance, however, is complex. The role and experience of self-compassion within perceived important competitive events are important to explore, as athletes face unique pressures and stressors in these meaningful sport experiences. This collective case study describes women athletes' self-compassion, sport performance perceptions, and well-being around a self-identified important competitive event. Competitive women athletes (N = 9) participated in two one-on-one interviews, before and after their important competitive event. Results from the holistic, functional, and thematic analyses are represented by holistic case descriptions and an overarching theme, Continuing to Excel in Sport, and subthemes, Reframing Criticism and A Determined Approach. In important competitive events, women athletes utilize self-compassion to promote performance perceptions and well-being when preparing, competing, and reflecting to excel in sport.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.033
GPT teacher head0.311
Teacher spread0.278 · 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

Citations26
Published2021
Admission routes1
Has abstractyes

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