The impact of transformational, transactional and laissez-faire leadership on the personal and psychosocial development of university student-athletes: A profile analysis
Bibliographic record
Abstract
Calls have been made (Turnnidge & Côté, 2016) for research on athletes' positive development related to coach leadership behaviours defined by the Full Range Leadership Model (FRLM; Avolio, 2011). Studies assessing FRLM behaviours have taken a variable-centered approach and have focused almost exclusively on one FRLM leadership behaviour (i.e., transformational coaching), with no research examining how combinations of leadership behaviours influence development. According to the FRLM, optimal development occurs when coaches use high transformational, moderate transactional, and low laissez faire leadership behaviours (Avolio, 2011). This study explored the relationship between FRLM coaching profiles (i.e., different combinations of transformational, transactional, and laissez faire) and athletes' positive developmental outcomes and negative experiences in university sport. A total of 605 Canadian university student-athletes (237 male, 368 female, Mage = 20.09) completed the Multifactor Leadership Questionnaire (Avolio & Bass, 2004) and the University Sport Experience Survey (Rathwell & Young, 2016) to assess coaches' leadership behaviours and athletes' development, respectively. Coaching profiles were compared on effectiveness using analysis of variance tests. Consistent with the FRLM, when athletes' perceived their coaches used high transformational, moderate transactional, and low laissez faire behaviours, they reported the lowest levels of negative experiences. Contrary to expectations, athletes experienced the highest levels of positive development when coaches used high transformational, moderate transactional, and moderate laissez faire leadership. The results support the use of high levels of transformational and medium levels of transactional leadership, but suggest laissez-faire leadership may associate with certain developmental outcomes when paired with transformational leadership.Acknowledgments: SSHRC
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".