Effects of short term PETTLEP-based cognitive specific imagery intervention on accuracy and efficacy of snap shot shooting of youth ice hockey players
Bibliographic record
Abstract
Several studies have proven a positive relation between PETTLEP-based imagery intervention and performance and many researchers argue that more studies should investigate its effect on a variety of sports (Holmes and Collins, 2001; Weinberg, 2008). Primary purpose of this study was to investigate the effects of a PETTLEP-based cognitive specific (CS) imagery intervention on performance and efficacy of a sport-specific task on youth hockey players aged between 13-15 years. A total of nine participants from a hockey club in southern Sweden were randomly assigned to an intervention and control group. All participants were administered a structured self-efficacy questionnaire and an adapted sport imagery questionnaire (SIQ) and were also tested for performance on an ice hockey shooting skill to determine baseline performance at the beginning of the experiment. This was followed by a 16-day intervention and at the end of the intervention participants were tested for the same task and administered the SIQ and self-efficacy questionnaire a second time. Although the descriptive statistics illustrated a positive change in performance scores of in intervention group the results from non-parametric tests did not show a significant effect of the intervention for all of the tasks included in the study. Hence, the present study was not able to draw a positive conclusion about PETTLEP-based imagery as previously established by similar studies. The findings of present study highlight the need for further research investigating effects of similar interventions on performance and efficacy of similar tasks in hockey using an improved protocol and a more controlled environment.Acknowledgments: Simon Graner, Erwin Apsitch, Sofia Bunke, Karin Moesch, Jonas Alm, Ulof Nilsson, Germano Gallicchio, Andres Thomsson, Maria Garcia, Pau Casas Bonet, Alexander Von Reppert, Jacob Lee Shafsky, and all players who participated in this study.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".