What do we know about stressors and coping among female athletes? A scoping review.
Bibliographic record
Abstract
There is extensive research examining stressors and coping in sport (Nicholls & Polman, 2007), with several studies suggesting gender/sex differences in the experiences of female and male athletes (Kaiseler & Polman, 2010). However, there have been few efforts to comprehensively synthesize the literature on stressors and coping among female athletes. Therefore, the purpose of this study was to conduct a scoping review of the literature on stressors and coping among female athletes. Five databases were systematically searched (PsychInfo, Scopus, Medline, and ProQuest Theses and Dissertations), followed by hand-searching of journals; 6,808 abstracts were screened for inclusion in the review (inclusion criteria: full text empirical articles, English language, research on stress and coping with female athletes). The analysis included examination of the topics that have been studied in relation to stress and coping among female athletes, the use of various methodological approaches, and identification of key findings and gaps in the literature. Researchers have investigated stress and coping in relation to several aspects of sport psychology including injury and performance, and gender/sex differences in perceived stress, competitive anxiety, and coping. Limited research has explored the mechanisms or reasons for gender/sex differences. There were a range of quantitative and qualitative methodological approaches, although there were few intervention studies to improve stressor appraisals or coping among female athletes. Key areas for future research include examining changes in stressors or coping over time while considering changes in athletes’ menstrual cycle, and developmental differences in female athletes’ stressors and coping across the lifespan.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".