“From everything to nothing in a split second”: Elite youth players' experiences of release from professional football academies
Bibliographic record
Abstract
Previous research has assessed the affects release from football academies has on psychological distress and athletic identity of players. However, there has been no qualitative research exploring players' experiences of the release process. This study retrospectively explored players' lived experiences of being released from a professional football academy, having completed a scholarship (from ages 16-18). Four male football players (age 21.6 ± 1.5 years) who had experienced release from professional academies participated in in-depth semi-structured interviews. Data were analyzed using Interpretative Phenomenological Analysis. Four super-ordinate themes were interpreted from the data: Foreshadowing release-"left out in the cold", The process of release, Support during the process of release and New beginnings-"there's a bigger world than just playing football every day". Players reported that their contract meeting was a traumatic experience, and they experienced psychological difficulties in the longer-term following release. Factors that compounded the players' release were: a lack of aftercare being provided by the players' professional clubs for their wellbeing, and a disuse of social support, which hindered their transition out of full-time football. Context relevant recommendations are made to help improve the release process for elite youth football players.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".