Freshman Fitteen: Effects of an online team building exercise intervention
Bibliographic record
Abstract
Previous research revealed positive relationships between team building (TB), cohesion, and group task satisfaction in exercise settings (Bruner & Spink, 2011; Carron & Spink, 1993). However, researchers have yet to examine the efficacy of an online TB intervention to foster these constructs in a similar activity context. The purpose of this pilot study was to examine the effectiveness of an 8-week online TB intervention, Freshman Fitteen, designed to enhance group cohesion, group task satisfaction and physical performance in first year university students. Twenty-seven participants (Mage=18.6, SD =1.3) completed questionnaires assessing cohesion and group task satisfaction and a series of physical fitness tests at baseline and following the completion of the 8-week intervention. After controlling for baseline assessments, results revealed significant increases in cohesion (ATG-T, ATG-S and GI-T), group task satisfaction and two measures of physical fitness (push-ups and vertical jump height) (ps. <.05). These findings extend previous TB research to an online platform and provide preliminary support for the use of online TB interventions as an effective group-based strategy designed to promote group cohesion, group task satisfaction and physical performance in an exercise setting. Acknowledgments: The project was supported by the Schulich School of Education, Nipissing University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".