The relationship between athletes' self-reported grit levels and coach-reported practice engagement over one sport season
Bibliographic record
Abstract
Grit is the tendency to work hard toward long-term goals, maintaining effort and interest despite challenges (Duckworth et al., 2007). Cross-sectionally, grit relates to achievement criteria outside sport (Credé et al., 2016) as well as practice amounts and skill level within sport (Tedesqui & Young, 2017). This study aimed to longitudinally examine relations between grit and practice engagement. At time 1, 14 athletes (10m, 4f; 13-23 yrs-old; weekly practice hours = 10.46, SD = 6.34) from two Canadian canoe/kayak clubs identified for having structured, competitive, and demanding programs, completed a survey for two grit facets (Tedesqui & Young, 2017): perseverance of effort (PE) and consistency of interests (CI). At times 1, 2 (one month later), and 3 (two months after time 1) coaches assessed each athlete's level of practice engagement (i.e., diligence, hard work, attitude, attendance). Results were plotted as performance profile cases or radar charts (Butler & Hardy, 1992). We examined representative cases to elucidate the suitability of each facet for explaining the variability/stability of practice engagement. Results highlight cases identified as (a) high PE at time 1, which were accompanied by evidence for stable levels of practice engagement (across the three time points); and (b) high CI at time 1, where no discernible patterns of association with measures of practice engagement and attendance could be gleaned. Discussion focuses on why the two grit facets differentially associate with measures of practice engagement longitudinally, and the value of using performance profile cases for observing such associations.Acknowledgments: This research was supported initially (2013-2016) by the Social Sciences and Humanities Research Council of Canada (SSHRC) through the Joseph-Armand Bombardier Canada Graduate Scholarship (767-2013-2136), and subsequently (2016-2017) by an Ontario Graduate Scholarship.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".