Considerations for Conducting Imagery Interventions in Physical Education Settings
Bibliographic record
Abstract
Abstract There is a need to develop effective physical activity interventions for children, given the growing concerns about physical inactivity and the related health issues (Colley et al., 2011). The Task Force on Community Preventive Services (2002) strongly recommends that school-based physical activity interventions could be an effective way to increase physical activity levels by modifying the social environment and the behaviours that take place within it. PE is not only an important source of physical activity (Sallis & McKenzie, 1991); it also provides important benefits regarding children’s psychosocial and motor skill outcomes, making them more likely to engage in physical activity into adolescence and adulthood (Sallis et al., 2012). We know that mental skills such as imagery have been shown to influence motivation, participation, and performance in motor learning, sport, and exercise (Hall, 2001). Therefore, the use of imagery in a PE context seems to be a natural extension of its traditional use in sport and exercise settings. The purpose of this review paper is to discuss factors to consider when designing and implementing an imagery intervention in a school-based PE setting. The recommendations will be discussed within three main categories: (a) the specific school context in which the intervention would be administered, (b) the design of contextually-appropriate and feasible methodology, and (c) individualizing imagery prompts to be sample- and situation-appropriate for the developmental level of the students as well as the PE context through which they would be delivered. Limitations as well as recommendations for future research or interventions conducted in PE settings will also be discussed.
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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 | 0.102 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".