Happy together? A qualitative investigation into team-influence on athletes' pre-game feeling states
Bibliographic record
Abstract
From a psychological perspective, athletes' pre-game feeling states are a major predictor of their within-game performance (Neil, Wilson, Mellalieu, Hanton, & Taylor, 2012). Consequently, to foster optimal in-game performance, research needs to identify which factors influence pre-game feeling states. For athletes competing in integrated sports (e.g., basketball, hockey, soccer, team handball, volleyball), one such factor might be their team (Eys, Hardy, Carron, & Beauchamp, 2003). In the present study, we aimed to explore this potential influence further. To this end, we interviewed nine male and five female athletes that competed in the above-mentioned sports at an intercollegiate or semi-professional club level. By way of inductive content analysis we classified athletes' responses into the main categories Aspects of Team-Influence and Antecedents and Moderators of Team-Influence. As major sub-themes of the former, we identified Active Team-Influence (e.g., positive reinforcement, tactical advice, open communication) and Passive Team-Influence (e.g., keeping a positive mood, being individually ready, respecting individual preferences). Team-Environment (e.g., unity, collective orientation, common focus), Leadership (e.g., senior players, informal roles, coaches), Explicit Strategies (e.g., open sharing, social bonding, team-video), and Individual Factors (e.g., preferences, competitive experience, roles) emerged as major sub-themes of the latter. In summary, our findings support the contention that the team influences integrated-sport athletes' pre-game feeling states. Therefore, both coaches and athletes in those sports should reflect on these influences and identify which actions and characteristics should be encouraged or curbed, respectively. To facilitate these efforts, further research should explore how the present findings link with established group-dynamics phenomena (e.g., social support, cohesion, norms). In addition, future research might want to explore whether and how the present findings extend to sports with different interdependence structures. Teams in sport are ubiquitous (Bruner, Boardley, & Cote, 2014), thus, their influence and potential power should be explored more fully.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".