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Enregistrement W6906399342 · doi:10.17605/osf.io/e8p4y

Effects of health promotion interventions targeting digital wellbeing among students in higher education institutions : a rapid review

2025· other· en· W6906399342 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2025
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHigher educationPsychological interventionDigital healthCitationHealth promotionPromotion (chess)eHealthAction (physics)

Résumé

récupéré en direct d'OpenAlex

Citation Van den Akker, O. R., Peters, G. Y., Bakker, C., Carlsson, R., Coles, N. A., Corker, K. S., Feldman, G., , Moreau, D., Nordström, T., Pfeiffer, N., Pickering, J. S., Riegelman, A., Topor, M., Veggel, N., Yeung, S., Mellor, D., & Pfeiffer, N. Generalized Systematic Review Registration Form. MetaArXiv.. https://doi.org/g5fj. Funding This project is financed by a research grant from Fonds de recherche du Québec / Société et Culture Program : research project [Projet de recherche] / concerted action [Actions concertées] / research program on screen use and health in young adults Grant number : 2024-2025 - 0UER - 359030 Funding period : 2024-2027 Background This rapid review aims to appraise, with a rigorous and critical approach, the state of scientific evidence on interventions to promote balanced screen use for post-secondary students. By promoting digital well-being, we mean anything that promotes a person's state of mental, physical and emotional health in relation to their use of technologies (smartphones, digital tablets, etc.) and digital media (e.g. Facebook, Instagram, Tik Tok). The term digital well-being, which will be used interchangeably with “balanced screen use” in this research project, may vary from person to person (Santé et bien-être numérique [Digital Health and Wellbeing], s. d.). In recent years, the use of screens has become an important, if not widespread, part of everyday life for students in higher education institutions (at college and university level), whether to pursue their studies, entertain themselves, develop and maintain social relationships or access the job market (Lozano-Blasco et al., 2022; Romero-Lopez et al., 2021; Saletti et al., 2021; 1). Consequently, a balanced use of screens is identified as a major challenge by these students (Di Genova, 2022; DiMartino et al., 2020; Saletti et al., 2021; Shankland et al., 2022), whose average age in Quebec varies between 18 and 25, depending on their academic level (Di Genova, 2022). These students are among the groups most at risk of experiencing harm from intensive screen use (Lozano-Blasco et al., 2022; Romero-Lopez et al., 2021; World Health Organization, 2015). Indeed, although balanced screen use has been associated with an increase in subjective well-being (Kardefelt-Winther, 2017, Rayan, 2017), prolonged, excessive use and the feelings associated with certain sites or uses can lead to significant physical and mental health harms (Gouvernement du Québec, 2022; Institut national de santé publique, 2021), as well as academic difficulties, relationship problems, social isolation and feelings of loneliness (Institut national de santé publique, 2021; Shi et al., 2022). These unwanted effects entail considerable costs for society, elevating screen use to the status of a “public health risk” (World Health Organization, 2015). While interventions to reduce the harm associated with excessive screen use are increasingly well documented, information on how to promote balanced screen use is much less developed (Saletti et al., 2021). This rapid review protocol on interventions to promote the digital wellbeing of higher education students or post-secondary students is part of a larger research project whose main purpose is creating a practice guide for promoting digital well-being among students in higher education in Québec, Canada. Other methodologies will be employed such as ongoing consultations with digital health experts who are also members of the research group, focus groups with various participants (e.g.: professionals and students) and a three-round Delphi with experts in digital wellbeing. This rapid review targets not only practices that are recognized as exemplary, but also those that are promising and emerging, especially considering that relatively few empirical research has focused on the promotion of balanced screen use. The results of this rapid review ultimately will be triangulated with data stemming from other research methodologies within the larger research program. Primary research questions 1.1 What are the effective health promotion interventions targeting digital wellbeing (or balanced screen use) among students in higher education institutions? 1.2 How effective are various health promotion interventions targeting digital wellbeing (or balanced screen use) among students in higher education institutions? 1.3 What are the common elements in the effectiveness of the interventions identified for balanced screen use among students attending higher education institutions, and the level of scientific evidence that can be attributed to them, in comparison with other approaches or in comparison with no intervention at all? 1.4 What variables are used to measure interventions promoting balanced screen use? 1.5 Is the effectiveness of interventions to promote balanced screen use maintained over time? Secondary research questions 1.6 What theoretical frameworks or approaches underpin these interventions? 1.7 Do the results differ according to the characteristics of students attending higher education institutions (gender, age, type of study, health problems, etc.)? References Di Genova L. La santé mentale des étudiants sur les campus canadiens: Les effets persistants de laCOVID-19. 2022; DiMartino NA, Schultz SM. Students and Perceived Screen Time: How Often Are Students in a Rural School District Looking at Screened Devices? Rural Special Education Quarterly. 2020;39(3):128‑37. Garritty C, Hamel C, Trivella M, Gartlehner G, et al. Cochrane Rapid Reviews Methods Group. Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness. BMJ. 2024 Feb 6;384:e076335. Kardefelt-Winther D. How does the time children spend using digital technology impact their mental well-being, social relationships and physical activity?: an evidence-focused literature review. 2017; Lozano-Blasco R, Robres AQ, Sanchez AS. Internet addiction in young adults: A meta-analysis and systematic review. Computers in Human Behavior. 2022;107201. Organisation mondiale de la santé. Public health implications of excessive use of the Internet, computers, smartphones and similar electronic devices: Meeting report. [Internet]. 2015. Disponible sur: http://apps.who.int/iris/bitstream/10665/184264/1/9789241509367_eng.pdf Rayan A, Dadoul AM, Jabareen H, Sulieman Z, Alzayyat A, Baker O. Internet use among university students in south West Bank: prevalence, advantages and disadvantages, and association with psychological health. International Journal of Mental Health and Addiction. 2017;15:118‑29. Romero-Lopez M, Pichardo C, De Hoces I, Garcia-Berben T. Problematic Internet use among university students and its relationship with social skills. Brain Sciences. 2021;11(10):1301. Saletti SMR, Van den Broucke S, Chau C. The effectiveness of prevention programs for problematic Internet use in adolescents and youths: A systematic review and meta-analysis. Cyberpsychology: Journal of Psychosocial Research on Cyberspace. 2021;15(2) Santé et bien-être numérique. C’est quoi le bien-être numérique ? https://sben.ca/ Shankland R, Gayet C, Richeux N. La santé mentale des étudiants: Approches innovantes en prevention et dans l’accompagnement. Elsevier Health Sciences; 2022. Stevens A, Hersi M, Garritty C on behalf of the Cochrane Rapid Reviews Methods Group, et al.Rapid review method series: interim guidance for the reporting of rapid reviews BMJ Evidence-Based Medicine 2025;30:118-123.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,029
score de la tête « metaresearch » (Gemma)0,081
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,153

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0290,081
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0080,009
Bibliométrie0,0130,012
Études des sciences et des technologies0,0010,001
Communication savante0,0050,004
Science ouverte0,0020,003
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

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.

Tête enseignante Opus0,059
Tête enseignante GPT0,432
Écart entre enseignants0,373 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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