Leadership challenges experienced by elite South African rugby coaches
Bibliographic record
Abstract
Orientation: As the leaders of teams that compete at the highest level, elite South African rugby coaches face constant pressures to consistently lead their teams to successful on-field performances. An understanding of the leadership challenges they face may highlight actions that could equip them to achieve this more effectively. Research purpose: To investigate the leadership challenges experienced by the head coaches of elite South African rugby teams that compete on an international level. Motivation for the study: The leadership challenges faced by elite South African coaches could become clearly known only through investigation, and subsequently they could be properly addressed. Research approach/design and method: A qualitative approach with a phenomenological design was utilised, which collected data by means of in-depth interviews with the head coaches of elite South African rugby teams. Eleven teams were considered to be elite South African rugby teams for this study given that they competed on an international level. Ultimately, six participants were included, representing 54.5% of the total population. The general systems theory was also used as a theoretical basis to present findings. Main findings: The data revealed three main themes, namely environmental, relationships and personal leadership challenges. The data revealed that these coaches experience significant leadership challenges, some of which are unique to the South African context. Practical/managerial implications: It is believed that the implementation of suggested recommendations will assist in ensuring both the economic survival and overall leadership improvement of coaches and the teams they lead. Contribution/value add: Theoretically the study added to the limited literature on leadership in elite South African sport and practically it provided recommendations to address the findings as well as for further research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".