Team Building in Sport: Linking Theory and Research to Practical Application
Bibliographic record
Abstract
This article provides a general overview and presents various effective methods for developing and implementing team building programs based on different desired outcomes in sport. Specifically, a background on team building is provided, followed by the presentation of different outcomes and benefits found in previous successful team building programs. A team building conceptual model (Carron & Spink, 1993 Carron, A. V. and Spink, K. S. 1993. Team building in an exercise setting. The Sport Psychologist, 7: 8–18. [Crossref], [Web of Science ®] , [Google Scholar]) and a four-stage approach (Carron & Spink, 1993 Carron, A. V. and Spink, K. S. 1993. Team building in an exercise setting. The Sport Psychologist, 7: 8–18. [Crossref], [Web of Science ®] , [Google Scholar]) are described. In addition, implementation and effectiveness of various interventions are discussed based on the recommendations from a number of researchers (e.g., Eys, Patterson, Loughead, & Carron, 2006 Eys, M. A., Patterson, M. M., Loughead, T. M. and Carron, A. V. 2006. “Team building in sport”. In Handbook of research in applied sport psychology: International perspectives, Edited by: Duda, J., Hackfort, D. and Lidor, R. 219–231. Morgantown, WV: Fitness Information Technology. [Google Scholar]). Finally, implications for practice are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".