Predicting Sport Experience During Training: The Role of Change-Oriented Feedback in Athletes’ Motivation, Self-Confidence and Needs Satisfaction Fluctuations
Bibliographic record
Abstract
Change-oriented feedback (COF) quality is predictive of between-athletes differences in their sport experience (Carpentier & Mageau, 2013). This study extends these findings by investigating how training-to-training variations in COF quality influence athletes' training experience (within-athlete differences) while controlling for the impact of promotion-oriented feedback (POF). In total, 49 athletes completed a diary after 15 consecutive training sessions to assess COF and POF received during training, as well as situational outcomes. Multivariate multilevel analyses showed that, when controlling for covariates, COF quality during a specific training session is positively linked to athletes' autonomous motivation, self-confidence and satisfaction of their psychological needs for autonomy and relatedness during the same session. In contrast, COF quantity is negatively linked to athletes' need for competence. POF quality is a significant positive predictor of athletes' self-confidence and needs for autonomy and competence. Contributions to the feedback and SDT literature, and for coaches' training, are discussed.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".