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Record W4388834308 · doi:10.1080/10413200.2023.2274458

“Everyone was sort of just like ‘ew’”: Adolescent athletes’ experiences of menstruation in sport

2023· article· en· W4388834308 on OpenAlexaff
Viktoria Keil, Margo E. K. Adam, Kacey C. Neely

Bibliographic record

VenueJournal of Applied Sport Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAthletesMenstruationPsychologyThematic analysisStigma (botany)CoachingDevelopmental psychologyClinical psychologyPhysical therapyMedicineQualitative researchPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Young girls often avoid being physically active due to physical and psychological symptoms of menstruation. Therefore, the purpose of the present study was to explore youth athletes’ experiences of menstruation in sport. Seven female athletes between 16 and 18 years old took part in semi-structured interviews. Interpretive Description methodology (Thorne, Citation2016) was used, and interview transcripts were analyzed using reflexive thematic analysis (Braun & Clarke, Citation2006, Citation2022). Athletes experienced several physical and emotional symptoms throughout the menstrual cycle which were perceived to impact performance. Overall, athletes felt there was not enough support available for dealing with menstruation due to stigma and lack of education. This also influenced coach-athlete communication, along with other factors, such as coach gender, age, and coach-athlete relationship. Moving forward, young athletes would like more support in the form of education for athletes and coaches, and more representation and destigmatization of the topic of menstruation.Lay Summary: This study explored adolescent athletes’ experiences of menstruation in sport. Following interviews with seven female athletes, we found that athletes faced a variety of challenges when dealing with their menstrual cycle in sport, but not enough support was provided due to stigma and a lack of education.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.343
Teacher spread0.316 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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