Exploring the relationship between psychological climate and athlete satisfaction across sex and competitive sport levels
Bibliographic record
Abstract
Researchers in organizational psychology report that team environments perceived as psychologically safe and meaningful (positive psychological climate [PC]) are associated with greater levels of job satisfaction (e.g., Brown & Leigh, 1996). While PC has been examined in the sport setting with respect to player effort, its relationship to player satisfaction has yet to be examined. Our purpose was to study the relationship between PC and satisfaction in sport, while examining sex and competitive level as possible moderators. Athletes (N = 343) from 24 intact sport teams completed a sport-adapted PC measure (Spink et al., 2013) and satisfaction with how teammates contribute to the individual as a person (i.e., social contribution; Riemer & Chelladurai, 1998) near the end of a competitive season. Given the nested nature of the data (ICC = .10), HLM was used to predict satisfaction from 4 dimensions of PC (i.e., supportive management, role clarity, self-expression, and contribution). The overall model was significant, ?2 = 55.05, p < .001, with role clarity (ß = .22) and self-expression (ß = .46) emerging as significant predictors (ps < .01) of satisfaction with social contribution. Neither sex nor competitive level emerged as significant moderators of the PC/satisfaction relationship. While in need of replication, these results provide a preliminary suggestion that athletes with a clear indication of role responsibilities and the ability to express individuality within the group also report greater social satisfaction. Further, it appears that the relationship is robust across males and females and more versus less competitive sport levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".