A commentary on high-performance athletes’ retirement and mental health: from mental health and transition literacy to athletic retirement literacy
Bibliographic record
Abstract
In this commentary we introduce athletic retirement literacy (ARL) as a concept and a novel approach for summarizing and structuring athletic retirement competencies to inform sport psychology practice. Meta-reflections from three recent papers about athletes’ mental health, transition literacy, and transition interventions (Schinke et al., Citation2024; Stambulova et al., Citation2025; Stambulova & Schinke, Citation2025) helped to formulate rationale for a specific focus on ARL. Following the promotion of mental health literacy as well as transition literacy among athletes and their supporters in this commentary we (a) define ARL among high-performance athletes as a set of basic competencies helping them and their supporters to understand the transition to a post-sport career from the individual and ecological perspectives, communicate about it, and make informed decisions and planning; (b) propose a schematic illustration of ARL structure with four major clusters of competencies, and (c) promote ARL-informed interventions. We believe that ARL-informed sport psychology practice will lead to reduced mental health concerns, improved general resilience, and a healthy path forward among retired athletes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".