Psychological climate and social support among male and female team athletes: Does perceiving the team environment matter?
Bibliographic record
Abstract
Perceiving a positive psychological climate has been associated with a higher degree of member engagement. In the sport setting, male athletes who perceived a positive psychological climate reported significantly more engagement in the form of greater self-reported effort. While climate has been examined with respect to player effort, its relationship to other types of engagement such as social support has yet to be examined. Further, given that sex differences have emerged in the climate literature in non-sport and sport settings, it was deemed important to explore climate relationships using both males and females. Our purpose was to study the relationship between climate dimensions (supportive management, role clarity, self-expression, contribution) and social support in male and female sport teams. To do this, male (N = 16) and female (N = 13) university volleyball athletes completed a measure of psychological climate and nominated players and coaches on their team who provided them with support to learn new technical, tactical, and mental skills. For males, results revealed that one of the climate dimensions (supportive management) was significantly related to receiving support from teammates for both technical and tactical skills (ps = .04). When male athletes perceived their coach supported them in how they carried out their roles, they were more likely to report receiving support from teammates for learning technical and tactical skills. No significant relationships emerged for females. While replication is needed, receiving support from teammates appears to be associated with how males perceive their team environment, but not females.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".