Differences in self-efficacy and performance as a result of attentional focus in a continuous running task
Bibliographic record
Abstract
Although self-efficacy and performance are typically studied as a relationship within continuous sport tasks (i.e., LaForge-MacKenzie & Sullivan, 2014a, 2014b), the complex and multifaceted nature of this relationship may result in differential effects of sociocognitive factors (e.g., attention) on self-efficacy and performance as separate constructs (Sitzmann & Yeo, 2013). The purpose of the study was to examine self-efficacy and performance separately under three conditions of attentional focus. Participants ran continuously on an indoor track for one kilometer in one of three conditions: internal-focus (n = 51), external-focus (n = 50), and control (n = 49). Self-efficacy was assessed simulatanously as performance using a one-item measure every 200 meters. One-way ANOVAs revealed significant differences in running performance at the start (F (2, 147) = 3.86, p < .05) and end of the task (F (2, 147) = 3.56, p < .05). The control group ran faster than the internal-focus group at the start of the task and faster than the external-focus group at the end of the task. Self-efficacy showed significant differences late in the task [Self-Efficacy 4: (F (2, 147) = 3.21, p < .05); Self-Efficacy 5: (F (2, 147) = 4.74, p < .05)], with the internal-focus condition having higher self-efficacy than the external-focus condition. These findings support suggestions that attention may shift throughout sport tasks (i.e., Schücker, Anheier, Hagemann, Strauss, & Völker, 2013), becoming increasingly internally focused as the intensity of continuous endurance tasks progress (i.e., Lima-Silva, Silva-Cavalcante, Pires, Bertuzzi, Oliveira, & Bishop, 2012). As such, an internal focus of attention may be beneficial to both self-efficacy and performance late in a running task.Acknowledgments: This research was made possible by the Social Sciences and Humanities Research Council of Canada
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".