Evaluation of the use of healing imagery in athletic injury rehabilitation
Bibliographic record
Abstract
Healing imagery can be defined as both visualizing affirmative images ofinternal physiological healing occurring in an injured area and visualizing oneselfas healthy and fully functioning (Driediger, Hall, & Callow, 2006). Healing imagery has been found to effectively reduce the time of recovery from various athletic injuries when used in combination with other mental skills, such as self-talk and relaxation (Ievleva & Orlick, 1991). However, the literature remains void of a study that specifically examines healing imagery and the potential benefits that may accompany the regular application ofhealing imagery techniques alone. This study examined the effects ofan imagery intervention on the recovery ofathletes experiencing an athletic injury in comparison to a control group who did not receive the imagery manipulation. The purpose of the present study was to determine the effectiveness ofa healing imagery intervention through comparing the two groups on: satisfaction with rehabilitation, self-efficacy to recover, and recovery time. The sample consisted of 13 injured varsity athletes (intervention group, n=6; control group, n=7) utilizing the athletic therapy services at Wilfrid Laurier University. A significant interaction effect was found for satisfaction with rehabilitation, as athletes’ in the intervention group increased in satisfaction from week 2 to 3 while the control group decreased in satisfaction during the same time period. The intervention group also used significantly more cognitive imagery than the control group. Both groups were found to be significantly higher in task self-efficacy than coping self-efficacy during injury rehabilitation. A follow-up qualitative analysis ofthe intervention group revealed that the healing imagery intervention positively affected athletes’ in a unique, individualized manner. Results are discussed with respect to a gained understanding of imagery effects, study limitations, and future directions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".