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Record W2786506971

The relationship between athletes' self-reported grit levels and coach-reported practice engagement over one sport season

2017· article· en· W2786506971 on OpenAlexaffabout
Rafael Ab Tedesqui, Bradley W. Young

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGritAthletesAttendancePsychologyDiligenceConsistency (knowledge bases)Facet (psychology)Applied psychologySocial psychologyMedicinePhysical therapyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.190
GPT teacher head0.397
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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