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Record W4413652878 · doi:10.3390/psycholint7030074

Addressing the Shortage of Women in High-Performance Sport: What Is Known and What We Need to Know

2025· article· en· W4413652878 on OpenAlexaboutno aff
Margaret E. Stone, Pippa Chapman, Urvi Khasnis, David Collins

Bibliographic record

VenuePsychology International · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic shortageNeed to knowBusinessPsychologyPublic relationsPolitical scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

The aim of this scoping review was to identify the attributes that have led female leaders and coaches to be successful in high-performance (HP) sport and uncover what may be missing elements aiding women wishing to advance in HP sport. To date, most research has focused on addressing the shortage of women in sports from the organizational perspective. This study investigates the challenges, and coping skills women have encountered on an individual level. Drawing attention to the skills and experiences of women who have been successful in attaining and maintaining their role in HP sport has the potential to help others advance in the field. Abstract screening (n = 411) and full-text reviews (n = 25) resulted in the inclusion of 16 studies. Included studies were conducted in the UK, Canada, Australia, the USA, and Europe, giving this review broad worldwide scope. Several areas of interest were revealed during data analysis: the challenges these women face working in the male dominated world of sport, how they faced and overcame those challenges and have maintained their role in HP sport, and the connectivity that has been established as a support mechanism for women in a leadership role. Lastly, these women discussed competency and knowledge of the sport as an important aspect of their tenure. What is absent from the literature, and exemplified in these data, is a clearly defined pathway into HP sport for the competent and knowledgeable female leader. The specific steps she can take are yet to be defined.

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 score0.305
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.377
Teacher spread0.337 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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