International Council for Coaching Excellence (ICCE) 14th Global Coach Conference
Bibliographic record
Abstract
Researchers have investigated coaching behaviors as predictors of athlete burnout; however, limited research exists on the relationship between athletes' perceptions of coach communication and their burnout.This study explored (a) the direct effects of athletes' perceptions of each coach communication on their sport and social competence; and (b) the indirect effects of athletes' perceptions of each coach communication construct on their burnout.A total of 291 high school athletes (M age = 15.72 years, 75.3% female, 84.2% White; 63.6% playing varsity) completed selfreport measures assessing four coach communication constructs (social support, useful feedback, prosocial power, conformation), sport competence, social competence, and burnout.Structural equation modelling (SEM) was run in Mplus Version 8.1.Results indicated adequate model fit (X 2 = 951.97;p < .001,TLI = .94;CFI = .95;RMSEA = .04[CI:.039-.048];SRMR = .06).Coaches' use of social support (β = 0.24; SE = 0.06; p < .001),useful feedback (β = -0.13;SE=0.08; p<.01), and prosocial power (β = -0.09;SE = 0.04; p < .05)significantly predicted athletes' sport competence, which predicted athlete burnout (β = -0.20;SE = 0.05; p < .001).Interestingly, only coaches' use of useful feedback (β = 0.18; SE = 0.06; p < .01)significantly predicted athletes' social competence, which in turn did not predict athlete burnout (β = -0.01;SE = 0.08; p = .877).The model accounted for 66% of the variance in athlete burnout.The indirect effects of athletes' perceptions on each coach communication construct indicated that coaches' use of social support (β = -0.25;SE = 0.06; p <. 001) and prosocial power (β = -0.13;SE = 0.03; p < .001)were significant predictors of athlete burnout.Our findings reinforce the complexity of coach communication and its critical role on athlete burnout.
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 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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".