The Nature and Consequences of Group Cohesion in a Military Sample
Bibliographic record
Abstract
The 1st objective of this study was to explore the dimensionality of the work-adapted version of the Group Environment Questionnaire (GEQ; Carron, Widmeyer, & Brawley, 1985 Carron, A. V., Widmeyer, W. N. and Brawley, L. R. 1985. The development of an instrument to assess cohesion in sport teams: The Group Environment Questionnaire.. Journal of Sport Psychology, 7: 244–266. [Crossref], [Web of Science ®] , [Google Scholar]) in a military setting. The 2nd was to investigate how group cohesion relates to job performance, job satisfaction, and psychological distress. To this end, 447 Canadian military employees who worked in units completed the GEQ, along with measures of job performance, job satisfaction, and psychological distress. Confirmatory factor analyses indicated that the hypothesized 4-factor model of the GEQ provided a better fit to the data than did alternative models. A path analysis indicated that perceptions of task-related cohesion were predictive of job satisfaction, whereas dimensions of cohesion reflecting attraction to the group were inversely associated with psychological distress. The relevance of social identity with respect to psychological dimensions and correlates of group cohesion is 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".