Interactive Effects of Goal Orientation and Perceived Competence on Enjoyment among Youth Swimmers
Bibliographic record
Abstract
This study investigated achievement goal orientation profile differences between youth swimmers on perceived competence and enjoyment, and the contribution of goal orientation and perceived competence to enjoyment in swimming. Male and female swimmers (n=302), aged 10-18 years (M=12.7; sd=2.25) completed a questionnaire assessing goal orientation, perceived competence and enjoyment in swimming. Cluster analysis revealed four goal orientation profile groups: high task/high ego, moderate task/low ego, high task/moderate ego and low task/moderate ego. MANOVA was conducted and a significant multivariate effect was found (Wilks=0.762; F=14.370; p=0.000; ES=0126). Further, Scheffe’s post-hoc comparisons tests revealed that swimmers scoring relatively high in both task and ego orientations, with a balance between the two, reported high values for perceived competence and enjoyment. Finally using two-way factorial MANOVA it was found that the interaction between the perceived competence and goal orientation profiles was not significant. Emphasizing task orientation for young athletes is a means to increase enjoyment in sport, regardless of their level of perceived competence.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".