Using imagery interventions to influence three types of self-efficacy for exercise
Bibliographic record
Abstract
The purpose of this study was to determine if guided imagery interventions could be used to enhance task, coping, and scheduling self-efficacy (SE) for exercise among a sample of female exercise initiates (N=232). Participants attended three guided imagery sessions administered by the researchers before beginning a 12-week cardiovascular exercise program. Control participants attended nutritional information sessions in place of the imagery intervention. The Multidimensional Self-Efficacy for Exercise Scale (Rodgers, Wilson, Hall, Fraser, & Murray, 2008) was used to assess task, coping, and scheduling self-efficacy for exercise. The effectiveness of the various imagery interventions for influencing the three types of self-efficacy over time were assessed with two doubly multivariate ANOVAs: the first from baseline to 6 weeks and the second from 6 weeks to 12 weeks. The first analysis demonstrated that in response to the different imagery interventions, the three types of SE were differentially influenced over time. The results of analysis two were non-significant suggesting that the main changes in SE occurred in the first six weeks of the intervention. It was concluded that task, coping, and scheduling SE for exercise are independent from one another and that mental imagery interventions can be used to influence exercise-related cognitions.Acknowledgments: Acknowledge the SSHRC for supporting this research
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".