Bouncing back: Does psychological resilience predict performance after failure on a sports task?
Bibliographic record
Abstract
Introduction: An athlete’s ability to be resilient, or “bounce back”, from failures and prevent a “downward spiral” of poor performances is key to being successful in sports. We sought to test this in a controlled experiment. Method: In this study, 62 participants executed 40 dart-tosses each, aiming for the bulls-eye of a regulation dartboard. Performance in terms of accuracy was defined as the distance between the bulls-eye and where the dart landed for each toss. Participants also completed the Connor-Davidson Psychological Resilience Scale (Connor & Davidson, 2003). Data Analysis: Mean accuracy scores for each participant were calculated; in addition, participants’ “poor” tosses (defined as tosses that landed more than 1 standard deviation from their average toss distance), “poor toss streaks” (the number of consecutive poor tosses, as defined above), and worst toss were recorded. After controlling for participants’ mean accuracy, separate linear regressions were conducted to test whether resilience predicted accuracy in the toss(es) after participants’ (a) poor tosses, (b) poor toss streaks, and (c) worst toss. Results: Compared to those with lower resilience scores, participants with higher resilience scores had shorter poor toss streaks (p = .02). Resilience approached significance as a predictor of accuracy after participants’ worst toss (p = .06). Resilience did not predict accuracy after a poor toss (p = .68). Discussion: These results provide partial support for the importance of being resilient in sports performance.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".