MétaCan
Menu
Back to cohort
Record W2945839889

Female athletes' experiences of positive growth following deselection in sport

2018· article· en· W2945839889 on OpenAlexaff
Kacey C. Neely

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAthletesPosttraumatic growthIce hockeyInterpretative phenomenological analysisPsychologyIdentity (music)Applied psychologySocial psychologyMedicineSociologyPhysical therapyQualitative researchSocial scienceArtAesthetics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to explore female athletes' experiences of positive growth following deselection from provincial sport teams. Interviews were conducted with 18 females (M age = 22.45 years, SD = 1.38) who were deselected from provincial soccer, ice-hockey, and volleyball teams as adolescents. Interpretative phenomenological analysis methodology (Smith, Flowers, & Larkin, 2009) was used. Analysis was guided by Tedeschi and Calhoun's (2004) model of posttraumatic growth. Results showed that participants questioned their identity and ability as an athlete following deselection. Growth was a gradual process that unfolded over several years, experienced through a greater appreciation of the role of sport in their lives and sport becoming a priority, an enhanced sense of personal strength, developing closer social relationships, and a recognition of new and other opportunities. These findings demonstrate the applicability of a model of posttraumatic growth in sport, and show that cognitive processes and social relationships are critical components in the process of positive growth.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository)Same topicSports injuries and preventionFrench-language works237,207