Factors influencing sports talent development in Senior High Schools in the Upper East Region of Ghana
Bibliographic record
Abstract
Despite the increasing interest in examining predictors within sports talent development, there is a dearth of studies in the Ghanaian context. Yet, Senior High Schools (SHSs) in the Upper East Region of Ghana are expected to help talented student-athletes grow their potential. This necessitates empirical research on factors that predict sports talent development within the Ghanaian context. Against this background, this study examines factors influencing sports talent development in SHSs in the Upper East Region of Ghana. Using a questionnaire, data was collected from two hundred and three (203) sports coaches (123 male and 80 female) since the core responsibility of developing sports talents lies with them. Multiple regression analysis was calculated using SPSS version 25 to identify the factors that predict the development of talents in sports. It was found that adequate sports facilities significantly made the largest contribution (β = .300, p<.05), followed by expert coaching (β = .271, p<.05), regular training (β = .260, p<.05), and family support (β = .151, p<.05). However, genetics made only small and not significant contribution. Therefore, it was recommended that coaches should encourage and guide talented athletes to train regularly to develop their talents. Again, SHSs should provide talented student-athletes with adequate sports facilities and equipment, and qualified coaches to help them develop their sports talent. Also, parents, siblings, and friends should provide support to talented student-athletes in any way possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".