Investigating high performance perfectionist athletes' perceptions of the junior to senior sport transition
Bibliographic record
Abstract
Whether or not perfectionism is a healthy personality characteristic is a topic of much debate among researchers (Flett & Hewitt, 2005). One way to investigate this is to differentiate how ?healthy? or ?unhealthy? perfectionists perceive achievement demand s. During their athletic careers, athletes experience the achievement demands of transitioning to higher levels of performance. One of the most demanding of these transitions is the junior to senior sport transition. As of yet, there is no research regarding how perfectionists experience and perceive \nthe junior to senior sport transition. Therefore, the purpose of this study was to explore how healthy and unhealthy perfectionist athletes experience the transition from junior to senior sport. This study employed a sequential mixed method design. In step 1, 27 current and former members of a high-performance cross-country skiing training program completed the Sport Multidimensional Perfectionism Scale-2 (Sport-MPS-2: Gotwals & Dunn, 2009), and coaches rated the athletes? respective levels of perfectionistic strivings and perfectionistic concerns. Using intra-sample and inter-sample criteria scores for the Sport-MPS-2, and the coaches? ratings, the athletes were designated as either healthy perfectionists, unhealthy perfectionists, or non-perfectionists. However, these characterizations did not yield a sufficient number of participants to compare healthy and unhealthy perfectionists. Therefore, the focus of the study \nwas redirected toward athletes with high perfectionistic strivings, which is one of the overarching dimensions of perfectionism (Stoeber & Otto, 2006). In step 2, using an open-ended interview guide, six athletes who met the criteria of high perfectionistic strivings were interviewed regarding their respective experiences and perceptions of the junior to senior sport transition. Inductive thematic analysis was used to analyze the qualitative data. Eight themes emerged from \nthe analysis: Balancing Priorities, Expectations as National Training Program Skiers, Overthinking and Overdoing, Team Dynamics, Competition, Poor Performance and Related Emotions, Negative Effects of Training, and Hindsight. These themes are contextualized within the stress process, the athletic career transition literature, and the perfectionism literature. The implication of this study is that perfectionistic strivings should be accounted for during the junior \nto senior sport transition.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".