THE RELATIONSHIP BETWEEN FORMS OF MOTIVATION AND MENTAL SKILLS IN PHYSICAL EDUCATION AND SPORT
Bibliographic record
Abstract
Motivation and mental skills have an important place in learning and performance. The objective of this work is to study the relationship between different forms of motivation and mental skills in physical education and sport.202 high school students including 100 boys (mean age = 17.2 1.2 years) and 102 girls (mean age = 16.4 1.3 years) participated in this study. In order to assess mental skills, we used the Ottawa Mental Skills Assessment Tool-3 test; and to assess different forms of motivation, we used the Scale of Motivation in Sports-28.The principal component analysis identified three components that represent 53% of total inertia. We named these components: intrinsic commitment, cognitive-emotional control and extrinsic commitment. At the end of this study, there is a strong relationship between different forms of motivation and mental skills in physical education and sport.There is a strong relationship between mental skills and different forms of motivation. These are strongly linked neuropsychological processes. More precisely, developing students' mental skills is developing their motivation. Therefore, the pedagogical interventions of teachers of physical and sports education must encourage the development of these neuropsychological skills, through a more suitable content that affects both the motor, cognitive and emotional aspects of the student. In perspective, there is a need to develop a test that assesses both mental skills and forms of motivation, the components of which will be intrinsic commitment, cognitive-emotional control and extrinsic commitment.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".