Using a Multilevel Approach to Examine the Relationship between Task Cohesion and Team Task Satisfaction in Elite Ice Hockey Players
Bibliographic record
Abstract
Of numerous studies conducted over the years examining cohesion in the sport setting, very few have acknowledged that participants are nested within teams, which has resulted in analysis of data at the individual level. Given that members of sport teams are interdependent, valuable information might be lost if constructs such as cohesion are examined only at an individual level. The purpose of this study was to illustrate how multilevel modeling could be used to handle this interdependence among observations within teams when examining the relationship between task cohesion and team satisfaction. Male ice hockey players (N = 194) on 10 teams completed the cohesion and satisfaction measures near the end of the regular season. Using multilevel analysis, task cohesion predicted variance in team task satisfaction at the individual (33%) and group (55%) levels. Results highlight the value of multilevel models as well as extend research finding a relationship between cohesion and individual satisfaction to team satisfaction.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".