Toward a conceptualization of good parenting in youth sport
Bibliographic record
Abstract
The purpose of this study was to conceptualize good parenting in youth sport. We addressed two research questions: 1) What do coaches perceive as good parenting in youth sport? 2) How do 'exemplary' parents support their children in youth sport? First, individual semi-structured interviews were conducted with 8 coaches (3 females, 5 males, M age= 40.1 years, SD = 15.1 years) who coached hockey (n = 4), volleyball (n = 2), basketball (n = 1), and soccer (n = 1). Coaches were asked to describe what they perceived to be good parenting in youth sport and to nominate exemplary parents they had dealt with who personified good parenting. Second, individual semi-structured interviews were conducted with 10 exemplary parents (7 mothers, 3 fathers, M age= 48.5 years, SD= 4.0 years). Parents were asked about their involvement in their child's sport, their general parenting style, and the specific parenting practices in which they engage. Interpretive description methodology was used. In this study, parents were attentive to their children's emotional experiences. They expressed this through three main categories: Shared goals, principles of an understanding emotional climate, and enhancing practices surrounding competition. Thus, we conceptualized that good sport parents are emotionally intelligent and understand their children, themselves, and the sporting context. They understand what to do, and when and how to do it, within a complex sporting milieu. From an applied perspective, these findings may be useful for informing future policies and programs to help parents support their children in sport.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".