The importance of physical proximity for team cohesion – a case study of USA Rugby 7s
Bibliographic record
Abstract
Research Question This paper seeks to contribute to the theoretical understanding of team cohesion in sport. While a robust foundation of research on team cohesion in sport exists, there is a dearth of research examining the role of physical proximity. With physical group exercise temporarily suspended due to COVID-19, herein lies an opportunity to examine team cohesion throughout different stages of physical distancing.Research Methods A single case mixed method study was employed comprised of semi-structured interviews (19 total) conducted at three different time points (September 2019; March 2020; June 2020) and a baseline/post administration of the GEQ Survey (September 2019 (N = 26); August 2020 (N = 27)). Qualitative data were analysed in NVivo 12, and survey data were analysed via paired t-tests.Results and Findings Levels of team cohesion remained stable throughout the season and during physical distancing on all three cohesion sub-scales (i.e. ATG-T, GI-S, GI-T). Three qualitative themes emerged: task and collective loyalty, resilience through social cohesion, and digital engagement.Implications Digital communication can temporarily fill the void of face-to-face interaction but cannot replace it long-term to build team cohesion. Adding physical proximity to the theoretical conceptualization of team cohesion makes the model more contemporary and especially relevant during times of physical distancing (e.g. pandemic, off-season, remote teams).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".