Assessing the Effectiveness of Self-Talk Interventions on Endurance Performance
Bibliographic record
Abstract
Self-talk in sport has been widely researched with somewhat conflicting results (Van Raalte et al., 1995 Van Raalte, J. L., Brewer, B. Brewer, Lewis, B. Lewis, Linder, D. Linder, Wildman, G. and Kozimor, J. 1995. Cork! The effects of positive and negative self-talk on dart throwing performance.. Journal of Sport Behavior, 18: 508–58. [Google Scholar]; Perkos et al., 2002 Perkos, S., Theodorakis, Y. and Chroni, S. 2002. Enhancing performance and skill acquisition in novice basketball players with instructional self-talk.. The Sport Psychologist,, 16: 368–383. [Crossref], [Web of Science ®] , [Google Scholar]). The purpose of this study was to assess the effectiveness of three different self-talk interventions on endurance performance. Participants were nine cyclists who performed a 20-minute cycling ergometer workout two times per week for five weeks. At each workout participants were requested to cycle as far as possible. A multiple-baseline design was utilized, which after varying baseline lengths allowed for the implementation of one out of three self-talk interventions: self-regulated positive self-talk, assisted positive self-talk, and assisted negative self-talk. Results revealed a performance increase in all groups with the greatest increase being found in the assisted positive self-talk condition.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".