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Record W4406932771 · doi:10.15273/jue.v15i1.12372

Silent Struggles: Relative Energy Deficiency in Sport (REDs) in Female Athletes

2025· article· en· W4406932771 on OpenAlexvenueno aff
Veronica Szygalowicz

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2025
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesPsychologyGender studiesMedicinePhysical therapySociology

Abstract

fetched live from OpenAlex

Relative Energy Deficiency in Sport (REDs) is a condition caused by prolonged periods of reduced energy intake relative to expenditure, specifically in sport. Although estimates of prevalence vary, most of the athletic population likely suffers from REDs. This interview-based project examined the state of REDs knowledge, awareness, and practices in female athletes and those working with them. Female athletes were the primary focus given their relative absence from existing research. Results from this project suggest that most cases of REDs are caused by unintentional nutrient restriction with parents, society, and social media spreading poor nutrition information and behaviors that athletes eventually adopt. Identifying and treating REDs is complicated by the need for cooperation from athletes, who may be unaware of their inadequate fueling practices or are intentionally hiding their restrictive behaviors. Obtaining an official diagnosis is often a complex and lengthy process, with many healthcare professionals working together to exclude other potential medical conditions. Overall, this study suggests that at-large REDs education is necessary for athletes and those working with them. Additionally, implementing REDs screening practices, employing dietitians, and making support and similar resources available at sports institutions may decrease the prevalence of REDs and expedite the identification and treatment process.

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 score0.527
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.328
Teacher spread0.297 · 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
Published2025
Admission routes1
Has abstractyes

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