Coaching athletes on the path to expertise: Strategies to foster conscientiousness, grit, and self-control
Bibliographic record
Abstract
Conscientiousness, grit, and self-control are athlete personality characteristics that have been shown to differentially predict criteria of expertise development, including deliberate practice and higher skill levels (Tedesqui & Young, 2018; Toering & Jordet, 2015). Little is known about coaches' views on (a) whether these conscientiousness-related traits are more stable/malleable, (b) strategies that can help athletes develop these traits, or (c) whether coaches consider these traits when making talent identification and development (TID) decisions. To fill these gaps, semi-structured interviews were conducted with 11 high-performance coaches (9 male, 2 female), representing individual (5) and team sports (6), at national and international competitive levels. Data were analyzed using inductive thematic analysis (Braun & Clarke, 2006). Although coaches viewed these traits as mostly stable, they also considered them amenable to development. To help athletes develop grit/perseverance, coaches exposed athletes to failures and created challenging training conditions. Other strategies to foster conscientiousness-related traits included building good training habits and having honest one-on-one conversations about athletes' behaviours. Prompted through a hypothetical scenario, coaches generally preferred to work with less talented athletes who displayed high levels of the investigated traits as opposed to talented athletes with lower trait levels; a high level of both trait and talent was identified as ideal. Coaches revealed not measuring but intuitively considering conscientiousness-related traits in their TID decisions, especially in the context of athlete development, but to a lesser extent at the highest competitive levels where winning took primacy. Results have implications for coaching and the development of desirable traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".