No professional athlete is an island: A case study exploring personal and family experiences of transitions through an early NHL career
Bibliographic record
Abstract
The careers of professional athletes are both exciting and tenuous. Normative and non-normative transition experiences (Stambulova, 2000) are both in play and how an individual is able to cope with these transitions is a key element to persistence and health (Stambulova et al., 2009). Throughout the course of an athletic career, several transitions will occur that affect the individual as well as significant others (Debois et al., 2012; Wylleman & Lavallee, 2004). In early work related to transitions, Schlossberg and colleagues (1981; 1995) point to four factors that could influence a person's ability to transition: 1) the situation, 2) the self, 3) support, and 4) strategies. Wylleman and Lavallee have also suggested a developmental approach to studying transitions through sport and highlight the roles of the individual and context throughout development to adulthood. The purpose of the current case study was to gain further understanding of transition experiences as they are currently happening throughout the early stages of a professional hockey career. Using a constructivist approach, a current NHL athlete as well as his parents and sibling were interviewed yearly over the past three seasons. Using previous frameworks, data collected were deductively analyzed to highlight or critique various components of the current models. Results point to the importance of continued family support throughout the early stages of a professional career and suggestions for individual and family preparation as an athlete enters the professional ranks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".