The Impact of Teammate Interactions on Team Cohesion in Collegiate Athletics
Bibliographic record
Abstract
Cohesion is considered to be one of the most important small group variables (Lott & Lott, 1965) and can be influenced by many individual and team factors (Carron & Spink, 1993). For instance, Carron and Spink (1993) theorized that teams with increased teammate interactions are more likely to experience high cohesiveness, yet no research to our knowledge has been conducted to empirically support this assumption. Therefore, the purpose of this study was to examine athletes’ perceptions of what constitutes an ideal amount of time interacting with teammates as well as the consequences of interacting frequently and infrequently in various team-related contexts. Participants were 165 athletes competing on varsity collegiate sport teams who completed an online questionnaire in which they were asked to report the minimum and maximum amount of hours per week that they would like to interact with teammates in various contexts. Descriptive statistics were calculated for interaction time within each context and a series of MANOVAs and t-tests were conducted to examine differences in the ideal amount of interaction time in each context based on gender, sport, year on team, amount of playing time, and living situation. Significant differences were found based on gender and living situation in a majority of contexts. The findings can be used to inform collegiate coaches and athletes concerning the ideal amount of interaction that should occur and how to navigate their athletes’ personal factors that may affect the preferred amount of time they want to spend with their teammates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".