A pre-longitudinal screen of performance in an integrated assessment of throwing and catching competence
Notice bibliographique
Résumé
Background The ability to adapt motor skill performance to a variety of conditions is vital to success in physical activity settings. Valid and reliable measurements of motor competence (MC) are critical for understanding its impact on physical health. Many widely used MC assessments evaluate the performance of isolated motor skills, place performers in highly standardized environments, and limit opportunities to adapt performance. Consequently, evaluations of motor skill performance using these tools may not adequately differentiate motor skill levels. Assessing MC in more dynamic performance contexts may enhance the utility of MC measures.Purpose The purpose of this study was to examine the developmental validity of scores on a combined throwing and catching assessment.Data Collection and Analysis Data were collected on a convenience sample (n = 873, nfemale = 320, age = 14.7 years ± 4.2). Participants threw a regulation tennis ball against a wall from a distance three times their standing height and caught it as many times as possible in 30 s. Performers were free to use any throwing pattern (e.g. over/underarm) or task strategy (e.g. speed, trajectory of ball, ball bouncing, one- or two-handed catch). The highest number of catches between two 30-second trials was used for analysis. Descriptive statistics were calculated for the total sample and for males and females separately. Pearson correlations were used to evaluate the relationship between throw-catch assessment (TCA) scores and age. We evaluated the relationship between TCA score, sex, and age via hierarchical regression with TCA score as the dependent variable using three models: (1) Participant sex as the sole predictor, (2) sex and age as predictors, and (3) sex, age, and the quadratic term for age (age2). Additionally, we evaluated the reliability of scores on TCA assessment trials via intraclass correlations (ICCs) using a two-way, fixed effects model.Results The mean TCA score was 11.34 (± 5.44; Mfemales = 9.18 ± 4.80, Mmales = 12.59 ± 5.40). Strong relationships were observed between TCA score and age (r = .743, rfemales = .698, rmales = .746; all significant at p < .001). Significant increases in model fit were observed at each stage of the hierarchical regression (Model 1: R2 = .091, p < .01; Model 2: ΔR2 = .484, p < .01; Model 3: ΔR2 = .009, p < .01), with the final model indicating significant effects for sex (βmale = 1.95, p < .01), age (β = 2.01, p < .01) and age2 (β = –.04, p < .01) on TCA score (R2 = .584, F3,869 = 406.7, p < .001). Finally, there was good reliability between scores on the two TCA trials (ICC = .875, F872,873 = 14.97, p < .001).Discussion These data provide preliminary evidence for the developmental validity of the TCA across childhood to young adulthood. The ability to differentiate skill levels across a wide age-range can enhance the ability to track MC across the lifespan and better understand the relationship between covariates of MC across the lifespan. These data provide support for a MC assessment that can be used by researchers and practitioners to evaluate throwing and catching skill in a swift and ecologically valid way.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».