Mental skill levels of South African male student field hockey players in different playing positions
Bibliographic record
Abstract
Research from other sporting codes highlighted the importance of taking positional differences into consideration when developing mental skills training (MST) programmes, due to the different requirements of each playing position. The purpose of this study was to determine the possible positional differences in mental skill levels among 91 tertiary institution male field hockey players. The participants competed in the University Sport of South Africa (USSA) tournament and were categorised into four positional groups [i.e., goalkeepers (n = 12), forwards (n = 24), midfielders (n = 25) and backs (n = 30). The participants completed the Ottawa Mental Skills Assessment Tool (OMSAT-3), which measures 12 mental skill subscales. The positional results were compared by means of effect sizes (expressed as Cohen‘s d-value) and yielded 18 moderate and 13 large practical significant differences among the four positional groups. Collectively, these results show that the goalkeepers had the lowest scores for seven of the 12 tested mental skills, whereas the midfielders outperformed the other positional groups in six of the 12 tested mental skills. From the results, it can be concluded that positional demands and differences should be taken into consideration when developing and implementing MST programmes for field hockey players.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".