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Enregistrement W4377085533 · doi:10.2196/45857

Virtual Risk Management—Exploring Effects of Childhood Risk Experiences through Innovative Methods (ViRMa) for Primary School Children in Norway: Study Protocol for the ViRMa Project

2023· article· en· W4377085533 sur OpenAlexaffvenue
Ellen Beate Hansen Sandseter, Ole Johan Sando, Håvard Lorås, Rasmus Kleppe, Lise Storli, Mariana Brussoni, Anita Bundy, David C. Schwebel, David J. Ball, Monika Haga, Helen Little

Notice bibliographique

RevueJMIR Research Protocols · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueInjury Epidemiology and Prevention
Établissements canadiensBC Children's HospitalLearning PartnershipUniversity of British Columbia
Organismes subventionnairesNorges ForskningsrådNorges Teknisk-Naturvitenskapelige Universitet
Mots-clésRisk managementPsychologyApplied psychologyPoison controlHuman factors and ergonomicsRisk assessmentProtocol (science)Suicide preventionMedical educationDevelopmental psychologyMedicineComputer securityComputer scienceMedical emergencyBusiness

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Research indicates that risky play benefits children's risk assessment and risk management skills and offers several positive health effects such as resilience, social skills, physical activity, well-being, and involvement. There are also indications that the lack of risky play and autonomy increases the likelihood of anxiety. Despite its well-documented importance, and the willingness of children to engage in risky play, this type of play is increasingly restricted. Assessing long-term effects of risky play has been problematic because of ethical issues with conducting studies designed to allow or encourage children to take physical risks with the potential of injury. OBJECTIVE: The Virtual Risk Management project aims to examine children's development of risk management skills through risky play. To accomplish this, the project aims to use and validate newly developed and ethically appropriate data collection tools such as virtual reality, eye tracking, and motion capturing, and to provide insight into how children assess and handle risk situations and how children's past risky play experiences are associated with their risk management. METHODS: We will recruit 500 children aged 7-10 years and their parents from primary schools in Norway. Children's risk management will be measured through data concerning their risk assessment, risk willingness, and risk handling when completing a number of tasks in 3 categories of virtual reality scenarios: street crossing, river crossing, and playing on playground equipment. The children will move around physically in a large space while conducting the tasks and wear 17 motion-capturing sensors that will measure their movements to analyze motor skills. We will also collect data on children's perceived motor competence and their sensation-seeking personality. To obtain data on children's risk experiences, parents will complete questionnaires on their parental style and risk tolerance, as well as information about the child's practical risk experience. RESULTS: Four schools have been recruited to participate in data collection. The recruitment of children and parents for this study started in December 2022, and as of April 2023, a total of 433 parents have consented for their children to participate. CONCLUSIONS: The Virtual Risk Management project will increase our understanding of how children's characteristics, upbringing, and previous experiences influence their learning and ability to handle challenges. Through development and use of cutting-edge technology and previously developed measures to describe aspects of the children's past experiences, this project addresses crucial topics related to children's health and development. Such knowledge may guide pedagogical questions and the development of educational, injury prevention, and other health-related interventions, and reveal essential areas for focus in future studies. It may also impact how risk is addressed in crucial societal institutions such as the family, early childhood education, and schools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45857.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,018
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Protocole · Signal consensuel: Protocole
Score de désaccord entre enseignants0,549
Score d'incertitude au seuil0,842

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0180,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,277
Tête enseignante GPT0,612
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreProtocole

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations18
Publié2023
Routes d'admission2
Résumé présentoui

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