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Record W4408227485 · doi:10.3390/dietetics4010011

‘Uncomfortable and Embarrassed’: The Stigma of Gastrointestinal Symptoms as a Barrier to Accessing Care and Support for Collegiate Athletes

2025· article· en· W4408227485 on OpenAlexaff
Jennifer A. Jamieson, Cayla Olynyk, Ruth Harvie, Sarah A. O’Brien

Bibliographic record

VenueDietetics · 2025
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsAthletesStigma (botany)PsychologyMedicinePsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

This study aimed to explore the occurrence of exercise-associated gastrointestinal symptoms (ExGIS) in collegiate athletes and identify related self-management practices. A sequential mixed methods design was used, consisting of an online survey followed by semi-structured interviews. Data were analyzed with descriptive statistics (survey) and thematic analysis (interviews). Survey respondents (n = 96) represented various individual and team sports but were primarily female (76%). ExGIS prevented or interrupted training and/or competition in 32%. Female athletes experienced gastrointestinal symptoms (GIS) more frequently at rest (60%) and during training (37%), compared to males (22% and 9%, respectively; p < 0.01). Only 12% sought health care for ExGIS. Four (13%) female runners with ExGIS agreed to an interview. Response rates and interview data provided evidence of stigma in discussing GIS. Self-imposed food restriction was a common self-management strategy. In summary, female collegiate athletes may experience a greater burden of GIS and ExGIS than males. The stigmatized topic of ExGIS is a potential barrier to seeking health care and support. Access to a sport dietitian could help to address barriers to ExGIS care and support self-management practices in these athletes.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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