The relationship between imagery use and mental toughness in athletes with a disability
Bibliographic record
Abstract
Imagery is an important mental strategy used for both cognitive and motivational purposes by athletes of varying ages (Gregg & Hall, 2006) and abilities (Shearer et al., 2007). Similarly, mental toughness is also considered an important psychological skill in obtaining athletic excellence (Jones et al., 2007). Despite the importance of these two constructs, only one study has examined the relationship between imagery use and the development of mental toughness (Mattie & Munroe-Chandler, 2012), and this was conducted with a sample of able-bodied athletes. It is equally important to determine if the relationship between imagery use and the development of mental toughness is evident for athletes of varying physical abilities. Therefore, the purpose of the present study was to examine the relationship between imagery use and mental toughness in athletes with a disability. Participants included 124 athletes with a disability ( M age = 31). Participants completed the Sport Imagery Questionnaire (Hall et al., 1998) and the Mental Toughness 48 Inventory (Clough et al., 2002). Hierarchical multiple regression analyses showed that imagery use significantly predicted mental toughness. Furthermore, motivational general-mastery imagery emerged as the strongest predictor for each dimension of mental toughness ( β = .33-.46). These findings provide further support for imagery use as a potential strategy for developing or enhancing mental toughness, not only in able-bodied athletes but also in athletes with a disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".