Development and initial validation of the perceived instrumental effects of violence in sport scale
Bibliographic record
Abstract
Introduction: A growing body of research is looking into risk factors for interpersonal violence (IV) in sport. This research suggests the existence of several important risk factors, especially organizational and social factors. One of these factors is the beliefs regarding instrumental effects of violence. Coaches may want to drive performance, deter failure, test resilience and commitment, develop toughness, assure interpersonal control, and promote internal competition. In sum, available evidence suggests the risk of IV increases when coaches believe in the effectiveness of strategies involving IV to enhance athlete performance or perceive external approval for these practices. Methods: The studies presented in this article seeks to develop and validate the Perceived Instrumental Effects of Violence in Sport (PIEVS) Scale in order to measure those beliefs in coaches. In study 1, item generation, expert consultation, cognitive interviews, pilot test and item reduction phases led to 25 items for the PIEVS around six dimensions. In study 2, exploratory factor analysis (EFA) was conducted with 690 coaches in order to determine the PIEVS factorial structure and the convergent and divergent validity of the scale was tested (long and short form). Results: Our results suggested a one-factor solution for the PIEVS (25 items). This one-factor model provided an excellent fit to the data and a very good internal consistency. The PIEVS and empowering motivational climate were negatively correlated, which supported divergent validity as expected. The PIEVS was positively correlated with the disempowering motivational climate and with sport ethic norms, which supported convergent validity as expected. Discussion: These findings provide preliminary evidence for the appropriateness of the PIEVS Scale to measure perceived instrumental effects of violence in coaches.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".