Girls, Women, and Female Athletes in Sport Psychology: A Decade-Long Review of the Literature
Bibliographic record
Abstract
The underrepresentation of female research participants, women, and girls has been highlighted as an issue of concern within a variety of research areas and disciplines across academia. More specifically, this lack of visibility has contributed to widening knowledge gaps regarding these populations while also perpetuating and strengthening existing inequities. Given these concerns, the purpose of this review was to explore whether similar imbalances could exist within the sport psychology literature and, if so, what future research projects might be completed to rectify these issues. To do so, all articles ( n = 3,005) published between the years of 2011 and 2021 in five journals of sport psychology were assessed. Following an analysis of the relevant studies collected, it was found that more articles including all boys, men, and male athletes ( n = 343) were published within this time frame compared with articles including exclusively girls, women, and female athletes ( n = 155). Additionally, it also appeared that research working with girls, women, and female athletes was lacking: (a) in recreational sport, (b) at both young and older ages, and (c) within team sport contexts. Further, most of the studies assessed often conflated participant sex- and gender-descriptive terminology. As such, it is highly encouraged that researchers in sport psychology make greater strides to conduct purposeful and targeted research focusing on girls, women, and female athlete participants and their specific issues over the coming years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".