Notice bibliographique
Résumé
Dear Editor-in-Chief, The concept of physical literacy (PL) and developing physically literate youth has become a topic of discussion among many researchers, across a plethora of disciplines. PL is often used to describe the characteristics of an individual who has the knowledge, physical competence, motivation, and confidence to be physically active for life (1). Measuring this construct will ultimately prove to be difficult, as the multifaceted nature of physically literacy is hard to capture in one assessment tool. Given our research group’s keen interest in PL and the measurement of PL, we were extremely interested to read about the Dragon Challenge, a dynamic assessment tool of physical competence for children 10 to 14 yr of age (2). Although the Dragon Challenge is not a comprehensive assessment of all domains of PL, the authors certainly provide a novel and interesting approach to measuring one of the core domains of the concept—physical or motoric competence. Unlike most movement skill assessments, the researchers state that the Dragon Challenge provides a comprehensive measurement of physical competence as multiple series of motor skills are assessed (i.e., simple, complex and combined) in an authentic environment, thereby ensuring the most accurate measure of PL. This is in opposition, the authors argue, to assessments that measure discrete skills in isolation, performed in static, limited environmental conditions. It is here that they identify a number of different assessments such as the Test of Gross Motor Development-2 (3), Bruninks–Oseretsky Test of Motor Proficiency (4), Movement Assessment Battery of Children-2 (5), and the Physical Literacy Assessment for Youth (PLAYfun) (6). Indeed, this represents a broad category of measures, some of which were developed for clinical purposes, whereas others were more consistent with assessment of motor competence in general populations. We contend that the authors have not accurately represented several of these measures and as a result overstate the novelity and uniqueness of their new measure. For instance, the PLAYfun tool is not simply a measure of movement competence. The tool also quantifies other domains of PL such as competence and confidence. In addition, although the authors imply that PLAYfun is a test that involves discrete skills, this is an oversimplification. The PLAYfun tool examines multiple facets of each of the 18 movement skills included in the battery. For example, when assessing running in a square, assessors look for not only proper running form but also the ability to pivot and speed of the movement (6). Even with a test such as the BOTMP, which focuses mainly on motor proficiency, skills are combined together. For instance, upper body limb coordination, wherein skills such as the dribble are tested, requires bilateral coordination of the hands (4). Although the Dragon Challenge certainly adds an important, alternate approach to the assessment of PL, we must be careful not to set up false propositions about the novelty or uniqueness of the approach. This tends to exaggerate differences between measures and may lead to unnecessary confusion among researcher and practitioners who are looking for measures to use. Laura St. John John Cairney INfant and Child Health (INCH) Laboratory Department of Family Medicine McMaster University Hamilton Ontario, CANADA
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 | 0,046 | 0,273 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,016 |
| Communication savante | 0,012 | 0,013 |
| Science ouverte | 0,008 | 0,007 |
| Intégrité de la recherche | 0,032 | 0,050 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,014 |
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 ».