Imagery use by injured athletes: A qualitative analysis
Bibliographic record
Abstract
The purpose of this study was to expand our knowledge and increase our understanding of imagery use by athletes in sport-injury rehabilitation using a qualitative approach. The participants were 10 injured athletes who were receiving physiotherapy at the time they were interviewed. During the interviews, the athletes provided extensive information about their use of imagery during injury rehabilitation and it was clear that they believed imagery served cognitive, motivational and healing purposes in effectively rehabilitating an injury. Cognitive imagery was used to learn and properly perform the rehabilitation exercises. They employed motivational imagery for goal setting (e.g. imagined being fully recovered) and to enhance mental toughness, help maintain concentration and foster a positive attitude. Imagery was used to manage pain. The methods they employed for controlling pain included using imagery to practise dealing with expected pain, using imagery as a distraction, imagining the pain dispersing, and using imagery to block the pain. With respect to what they imaged (i.e. the content of their imagery), they employed both visual and kinaesthetic imagery and their images tended to be positive and accurate. It was concluded that the implementation of imagery alongside physical rehabilitation should enhance the rehabilitation experience and, therefore, facilitate the recovery rates of injured athletes. Moreover, it was recommended that those responsible for the treatment of injured athletes (e.g. medical doctors, physiotherapists) should understand the benefits of imagery in athletic injury rehabilitation, since it is these practitioners who are in the best position to encourage injured athletes to use imagery.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".