Applying transformational leadership theory to coaching research in youth sport: A systematic literature review
Bibliographic record
Abstract
There is growing recognition that Transformational Leadership theory (TFL) holds significant potential for coaching research in youth sport. While the existing literature offers insight into the types of athlete outcomes that are associated with TFL, studies evaluating how these outcomes can be acquired are limited. The purpose of the present study was to synthesise and integrate research across a variety of disciplines (e.g. organisational psychology, health care and promotion, education, and sport and exercise psychology) examining the processes by which TFL influences followers’ (i.e. employees, students, patients, athletes, etc.) psychosocial development. A systematic search was conducted of six electronic databases covering a wide range of disciplines. Peer-reviewed, original studies published in English were included in this review. The initial search yielded 2077 papers, of which 151 met the selection criteria and were retained for analysis. A descriptive, content analysis-based approach was used to assess emerging patterns in research design and study findings. Results revealed numerous processes at the intrapersonal, interpersonal, and environmental levels that contributed to the relationships between TFL and follower development. A conceptual model of these processes is proposed, along with recommendations for future coaching research in youth sport.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".