The Design, Implementation, and Evaluation of a Pilot Online Conflict Management Workshop for High School Sport Leaders
Bibliographic record
Abstract
Conflict and conflict management in sport have received less attention from researchers and practitioners compared to other settings (i.e., business, personal relationships). Studies have focused on athlete perspectives and team outcomes of conflict (Holt et al., 2012; Paradis et al., 2014a), but lack an exploration of explicit strategies for managing conflict. Further, peer leaders of sport teams struggle with facilitating relationships and managing conflict on their teams (Voelker et al., 2011). The purpose of this two-phase study is to explore conflict in sport and potential conflict management resources for youth athletes. In Phase 1, a needs assessment, two focus group interviews with high school team captains identified current sources of conflict, barriers to addressing conflict, and their use of specific conflict management strategies. These results and the COM-B framework (Michie et al., 2011) informed the design and implementation of an online conflict management workshop. In Phase 2, twelve high school student leaders from the same school participated in the online workshop. A mixed-method evaluation measured individual changes in two variables associated with conflict management (cognitive flexibility and problem-solving ability) through surveys and focus group interviews post-workshop. Results indicated this pilot workshop was effective in increasing perceptions of cognitive flexibility and problem solving (i.e., a more positive outlook on problems, a rational problem-solving style, and less avoidance of problems). Results also support the use of a novel framework for managing conflict. The success of this workshop shows promise for future implementation and offers a resource for adolescent athletes.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".