A preliminary examination of the correlates of role satisfaction
Bibliographic record
Abstract
The objectives of the present study were to examine (a) the potential correlates of role satisfaction, and (b) whether related role elements (e.g., role clarity, role efficacy, role importance) and team cohesion were predictors of role satisfaction within intercollegiate sport teams. Participants included 174 male and female intercollegiate basketball players ( M age = 20.85 ± 1.42 years). Initial correlation analyses revealed that role satisfaction was associated with role clarity ( r = .69), role importance ( r = .76), role efficacy ( r = .42), attractions to group-task ( r = .31), and attractions to group-social ( r = .25; all ps M sat = 6.56 ± 1.35) and higher satisfaction (i.e., upper 33%; M sat = 9.27 ± 0.38) groups. A simultaneous binary logistic regression analysis determined significant predictive relationships with respect to role efficacy ( ? = 1.03, p ? = 2.08, p = 0.001), role importance ( ? = 1.64, p ? = 0.87, p = 0.051), and attractions to group-social ( ? = 0.85, p < .05). The overall prediction success was 88%, with 86.5% of the low satisfaction cases and 90% of the high satisfaction cases correctly classified. Results suggest that role satisfaction is a salient perception among intercollegiate athletes and, furthermore, is closely related to important group processes such as team cohesion. Potential implications will be discussed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".