The influence of gender on coaches' use of psychological skills training
Bibliographic record
Abstract
The influence of gender in sport has been well documented (Fortier, Vallerand, Briere, & Provencher, 1995). Within the discipline of sport psychology, gender differences have been found in athletes' attitudes towards general psychological services (Martin, Wrisberg, Beitel, & Lounsbury, 1997), working with sport psychology consultants (Martin, 2005), and using sport psychology training on their own (Anderson, Hodge, Lavallee, & Martin, 2004). Similar findings have been reported with coaches (Zakrajsek & Zizzi, 2007). The present study examined the influence of athlete and coach gender on coaches' use of psychological skills training. Canadian curling coaches (n = 147) completed a revised version of Bull, Albinson, and Shambrook's (2002) Mental Skills Questionnaire (MSQ) which measures seven factors: imagery ability, mental preparation, self-confidence, anxiety and worry management, concentration ability, relaxation ability, and motivation. A 2 by 2 factorial MANOVA was run to see if the seven factors of the revised-MSQ differed by gender of coach or gender of athlete. There was no significant interaction, and no main effect for gender of coach. However, there was a significant (p < .001) main effect for gender of athletes. Follow up tests revealed that coaches reported a significantly greater frequency of each of these PST skills with female athletes than their male counterparts (p < .01). These results are consistent with previous research and suggest that gender of athlete may be a significant factor on psychological skills use by athletes. Implications for coaching education and sport performance are 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".