Sport and gender influences on coaches' encouragement of imagery among their athletes
Bibliographic record
Abstract
Certified and competitive coaches encourage imagery more than uncertified and recreational coaches (Hall et al., 2007; Jedlic et al., 2007). This study examined whether sport type and gender of both coaches and athletes also influence coaches' encouragement of imagery. Competitive athletes (n = 44 male, 45 female) from interactive (n = 57) and independent sports (n = 32) completed the Coaches' Encouragement of Athletes' Imagery Use Questionnaire (Jedlic et al.) and the enjoyment/improvement subscale of the Perceived Motivational Climate in Sport Questionnaire-2 (Newton et al., 2000). Data were analyzed using one-way MANOVAs. No sport type differences emerged for coaches' encouragement of imagery or motivational climate (p > .05). Female athletes felt coaches provided greater encouragement of imagery outside practice, before bed, and for CG and MG-M functions (p < .05). Overall, athletes felt female coaches provided more encouragement of imagery outside practice, before and during competition, and for the MG-M function (p < .05). Athletes whose coach taught psychological skills scored higher on effort/improvement and all CEAIUQ subscales (p < .05). While female athletes were not more likely to have a coach that taught psychological skills, a greater proportion of females reported that imagery was one of the skills taught by their coach. The findings suggest there may be a gender bias in athlete and coach perceptions of imagery and how it is encouraged among competitive 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".