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Record W2037378351 · doi:10.1300/j135v07n04_05

Elite Female Swimmers' Experiences of Emotional Abuse Across Time

2008· article· en· W2037378351 on OpenAlexaff
Ashley Stirling, Gretchen Kerr

Bibliographic record

VenueJournal of Emotional Abuse · 2008
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyAcquiescenceAthletesElite athletesElitePsychological abuseSport psychologyClinical psychologySocial psychologyDevelopmental psychologySuicide preventionPoison controlSexual abuseMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate athletes' experiences of emotional abuse. Semi-structured interviews were conducted with 14 retired, elite, female swimmers and data were analyzed inductively using a grounded theory approach. Results revealed that the athletes experienced emotional abuse in the coach-athlete relationship and that these experiences varied across an athlete's career. The same emotionally abusive behaviours were experienced differently at various stages of an athlete's career. Furthermore, an athlete's affective response to emotional abuse was dependent on the athlete's perceived performance and cultural acquiescence, as reflected in the phases of normalization and rebellion. A model of the process by which athletes experience emotional abuse over time is proposed. Implications of these findings are discussed and recommendations are made for future study.

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)0.005
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
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 head0.035
GPT teacher head0.337
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 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

Citations101
Published2008
Admission routes1
Has abstractyes

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