Exploring family dynamics in the development of a child-athlete: A longitudinal case study
Bibliographic record
Abstract
Family support is an important contributor to children’s healthy development in sport (Fraser-Thomas et al., 2013). While a growing number of studies have investigated the roles of parents and siblings in children’s sport involvement and talent development, few have explored the holistic influences of all family members (Cote, 1999). The purpose of this case study was to prospectively explore family influences in the development of a child-athlete over a three-year period. Participants included a female child-athlete, her mother, father, and coach. Semi-structured interviews were carried out at yearly intervals with all four participants over a three-year period, from the time the child was aged 7 to 9. The child athlete’s mother also completed a demographic and sport history questionnaire, and practices and competitions were observed at yearly intervals. Preliminary findings shed light on the parent-coach-athlete triad relationship, sibling rivalries, parental attitudes and behaviours towards each child within the family, potential benefits for parents of child-athletes, and contradictions in perceptions of family members. Findings of this case study provide a valuable longitudinal understanding of the family dynamics of one child-athlete over time, shedding light on important aspects of family support in the development of athletes throughout childhood, and highlighting key areas for future research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".