The Impact of Hockey Coaches and Team Cohesion on the Performance of Players
Bibliographic record
Abstract
The primary objective of the existing paper deals with to examine the relationship of hockey coaches and team cohesion with the performance of field hockey players of Pakistan and their impact on players’ performance. However the secondary objective was to measure the field performance of hockey players on practical measures. The research methodology is based on both descriptive and inferential statistical approaches. The descriptive data was collected in the form of field performance tests (technical skills and fitness capabilities) while the inferential data perceived by players was collected using survey questionnaire. A number of 296 national field hockey players of Pakistan were selected from 14 national departments of field hockey as samples. Descriptive statistics, correlation and multiple regression analysis were employed through SPSS (version 21.0). The results of the field performance tests were found below than average (weaker) in technical skills and fitness capabilities of national field hockey players of Pakistan. However, the findings of the inferential analysis revealed that hockey coaches and team cohesion have positive and significant relationships with the performance of field hockey players. Implications of existing study were also briefed.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".