A comparison of the psychological skills used by lower and higher level basketball officials
Bibliographic record
Abstract
Research investigating sport officials has examined their personality, the effect of audience presence on refereeing behaviours, and decision making (Askins et al., 1981; Brand et al., 2006). However, very little research has examined the psychological skills used by sport officials, despite its importance in the performance enhancement of athletes. This is somewhat surprising given the Cornerstones Performance Model of Refereeing identifies psychological skills as key in optimizing refereeing performance (Mascarenhas et al., 2005). The present study examined the psychological skills most frequently utilized by officials and whether there were differences between high (officiating varsity and higher) and low (officiating high school and lower) level officials. Participants were 450 Canadian male basketball officials who completed the Test of Performance Strategies (Thomas et al., 1999). The results indicated that basketball officials reported using psychological skills most to maintain their emotional control (M = 3.90) and least to help them relax (M = 2.80). With respect to differences in level of officiating, an overall effect was found (F (1, 449) = 6.21, p < .001, ?2= .10) with higher level officials reporting higher frequency of self-talk, emotional control, automaticity, imagery, activation, and negative thoughts than their lower level counterparts. Implication of these results 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".