Frameworks of Team Processes in Sport: A Critical Review with Implications for Practitioners
Bibliographic record
Abstract
Researchers have directly or indirectly examined team processes that contribute to team functioning and effectiveness in sport. However, in doing so, they have typically focused on team cohesion, they have not consistently addressed the theoretical/conceptual frameworks underpinning their work, nor have they comprehensively derived implications for practice. Furthermore, existing meta-analyses and reviews on cohesion and team building address results of empirical studies and do not evaluate the specific theoretical/conceptual frameworks used to guide these studies. Consequently, the purpose of this study was to critically review theoretical/conceptual frameworks directly or indirectly addressing team processes in sport and derive implications for professional practice. Seven frameworks used to guide research and/or practice in sport were identified for inclusion in this study. Three frameworks were borrowed from general psychology and the other four stemmed from sport psychology. These frameworks targeted a variety of specific team processes and six different outcomes, the most common of which was cohesion. Specific team processes were categorized under broader team processes, the latter of which were linked to one of ten general themes. The theme pertaining to roles/norms was the most prevalent one as it was addressed in six out of the seven frameworks. Conversely, one of the least prevalent general themes related to goals; it was only discussed in two of the seven frameworks. Implications for practitioners aiming to optimize team processes in sport and recommendations for future research are presented.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".