The practice of imagery: A review of 25 years of applied sport imagery recommendations
Bibliographic record
Abstract
Over time, researchers have advanced our understanding of sport imagery by providing theoretical, methodological, and practical recommendations (e.g., Munroe-Chandler & Hall, 2017). These practical recommendations often consist of a summary of the research findings, with the intent of enhancing applied practice. What is seldom discussed, however, is the use of these recommendations by those participating in, or facilitating, sport experiences (Gould, 2016). If sport imagery researchers are to infer that their recommendations will be effectively implemented, they are assuming practitioners have adequate knowledge of, and training in, imagery. Therefore, it is important to further examine these applied imagery recommendations and to evaluate their practicality. The purpose of the current study was to identify the most common practical imagery recommendations over the past 25 years. Imagery studies were identified from an electronic search and were included in the analysis if they examined imagery, used original data, and provided practical recommendations (n = 94). A content analysis was used to identify the number of studies that provided practical recommendations (e.g., Cope et al., 2011). Further, a reflexive thematic analysis was conducted to develop, construct, and generate commonalities in the data (Braun & Clarke, 2019). The most common recommendations included: (a) the role of the coach to facilitate imagery use, (b) the use of motivational imagery to increase confidence, and (c) the matching of the imagery function(s) to the desired outcome(s). Interestingly, similar recommendations appeared across multiple decades, suggesting that these recommendations are rarely followed in applied practice.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".