Evaluating the Efficacy of a Serious Game to Deliver Health Education About Invasive Meningococcal Disease: Clustered Randomized Controlled Equivalence Trial
Notice bibliographique
Résumé
BACKGROUND: Invasive meningococcal disease (IMD) is a serious, vaccine-preventable infectious disease that can be life-threatening. Teaching adolescents about the early detection and prevention of IMD can be challenging in a school environment, with educators reporting they lack confidence or expertise to cover this in the classroom environment. Professional guest educators are an alternative to cover specialist topics such as IMD; however, time and resourcing constraints can mean that these educators are not always available. Serious games may be an alternative to face-to-face education, where complex health information may be delivered via self-directed gameplay. OBJECTIVE: This study aims to develop a serious game that can replace a face-to-face educator in a classroom setting to educate adolescents aged 12 years to 15 years. This study evaluates the efficacy of the Meningococcal Immunisation Awareness, Prevention and Protection app (MIApp), a serious game designed to replicate the information provided in a 30-minute face-to-face presentation provided by a trained educator. METHODS: This clustered, randomized controlled equivalence trial involved students (Years 7-10) from 6 secondary schools across metropolitan Western Australia who completed pre- and postintervention questionnaires with a follow-up at 3 months postintervention to measure the primary outcome of IMD knowledge acquisition following this self-guided intervention. The findings were compared with changes in an active control (comparison) group who received an in-class educational presentation about IMD transmission and protection. A questionnaire was developed to assess 9 key areas of knowledge. Median scores for knowledge pre- and postintervention were collected from a self-administered assessment of this questionnaire and, at 3 months postintervention, were compared between groups. A knowledge score of +/-2/16 was determined a priori to meet the criteria for equivalence. Participants who used MIApp were also asked a series of questions to assess the enjoyment of and engagement with the game. RESULTS: Of the 788 participating students, the median postintervention correct score in both the MIApp and control cohorts was 14/16 (87.5% correct responses), compared with the median pre-intervention correct score of 6/16 (37.5% correct responses), representing a significant (P<.001) increase in IMD knowledge in both groups. Improvements were retained in both groups 3 months after the initial intervention (median correct score: 11/16 in the intervention group; 12/16 in the control group; P=.86), demonstrating the efficacy of MIApp to deliver health education about IMD transmission and protection, although response rates in the follow-up cohort were low (255/788, 32.4%). CONCLUSIONS: MIApp met the predetermined threshold for equivalence, demonstrating similar improvements in knowledge posttrial and at the 3-month follow-up. Participating adolescents considered the MIApp game more enjoyable than a presentation, with equivalent improvements in knowledge. Serious games could represent a constructive tool to help teachers impart specialized health education.
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 | 0,009 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».