Exploring How Soccer Players With Perfectionism Navigate Challenges in Talent Pathways
Bibliographic record
Abstract
The study provides a qualitative exploration of how soccer players reporting perfectionism navigate challenges in talent pathways. Eighteen players (10 females, eight males, Mage = 16.17 years, SD = 3.47) from talent pathways with higher levels of perfectionism and perfectionistic cognitions (1SD above the mean of samples from previous studies) participated in semistructured one-to-one interviews. Using semantic thematic analysis, seven themes were identified: cycles of anxiety, sadness at being a substitute, self-criticism and hopelessness during slumps, ruminating on mistakes, worthless when injured, shame in success and intolerance of defeat, and psychological distress. Participants experienced heightened anxiety, especially when substituted, and responded to poor performance, mistakes, and injuries with self-criticism and unhelpful emotions. Postmatch, they ruminated over both success and defeat, with some reporting extreme psychological difficulties. The findings highlight how aspiring soccer players perceived perfectionism as a barrier to overcoming challenges, hindering both their performance and well-being.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".