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Record W4220766442 · doi:10.15273/jue.v12i1.11316

Swimming Through Ambivalence: The Paradox of Vigilant Coping in Female College Athletes

2022· article· en· W4220766442 on OpenAlexvenueno aff
E N Shen

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsAmbivalencePsychologyCoping (psychology)DistressDisordered eatingRespite careConfusionSocial psychologyPhenomenonEating disordersDevelopmental psychologyClinical psychologyMedicineNursing

Abstract

fetched live from OpenAlex

Three decades of research into disordered eating in sports have revealed contradictory results, with athletes considered both at risk but also protected from eating problems. To contribute insight into the complexities of existing literature, this study examines how female college swimmers in the US experience food and their bodies in athletic, non-athletic, and digital media contexts. Drawing on 16 in- depth interviews, this study finds that these athletes experience contradictory ideals, especially in the domains of health and online self- presentation. When faced with such tensions, they often seek resolution through heightened discipline and attention towards controlling the body, a phenomenon that I call vigilant coping. While vigilant coping can provide some satisfaction and respite, it often exacerbates confusion and reproduces anxieties. As a result, a predominant strategy for resolving ambivalence becomes a source of additional distress around food and body. The women in this study show how athletic contexts contribute to the paradox of vigilant coping but also reveal creative ways of coping that go beyond vigilance. These findings are particularly important given the low success of disordered eating treatments and urgency towards improving preventative efforts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

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

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 head0.057
GPT teacher head0.360
Teacher spread0.303 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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