A qualitative analysis of female sport experiences in soccer
Bibliographic record
Abstract
Relative Age Effects (RAEs) have the potential to be counterproductive to sport participation rates given the associated selection (dis)advantages and inequitable access to development opportunities for individuals of varying relative age. Previous work has predominantly been quantitative in nature and focused on male athletes, with only a few qualitative studies in the published literature. Thus, the purpose of this study was to examine relative age and sport engagement and dropout issues by conducting a qualitative analysis of post-adolescent, female athletes' experiences. An invitation to participate in a semi-structured interview (via phone) or online questionnaire (via Qualtrics) was distributed to a targeted sample of post-adolescent (18–19 years of age), current and past female soccer participants from Ontario, Canada (N = 15). Questions focused on reasons for participation and dropout, aspects of programs and relationships that facilitated or discouraged participation, player recommendations for encouraging future participation or reuptake of the activity, perception of abilities at various stages, involvement in other sports, location considerations, and age issues. The three stages of Côté's Developmental Model of Sport Participation were used to structure the questions in order to explore experiences occurring during specific stages of the athlete's developmental years. Hierarchical content analysis was used to identify raw data themes, which were grouped into higher order sub-themes and categories. Half year comparisons (H1 vs. H2) revealed similar themes reported by relatively older and younger participants, suggesting relative age was not the most important factor with respect to the players' experiences and decisions to continue in the sport when examined from a qualitative lens, although study design may have been a contributing factor. Engaged athletes reported a greater number of themes related to specialization in sport, and dropout athletes reported more negative sport experiences. Sport sampling at young ages (<12 years of age) was associated with ongoing sport participation into the post-adolescent years, with engaged athletes reporting involvement in a greater number of additional sports (beyond soccer), vs. the dropouts. Community size/characteristics reportedly impacted sport experiences, although no clear trends were ascertained. General recommendations for sport practitioners and recommendations for future research are discussed.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".