Youth social networks and substance use prevention in Ghana: Exploring approaches to designing school-based preventive interventions
Notice bibliographique
Résumé
Background: Globally, harmful substance use among young people is a public health problem with gender-specific impact. According to a Global Disease Burden study, substance use prevalence increased by 76% between 1990 and 2019 among young people in Africa, including Ghana. Despite the increasing burden of substance use, school-based interventions for prevention are lacking. Given the influence of peer relations on young people’s behaviours, interventions that leverage their social networks may be effective. Goal: The goal of my doctoral research was to explore approaches for the development of school-based substance use prevention interventions in Ghana, with examination of gender and social networks. I conducted a multi-method research study comprised of three phases. Phase one was a scoping review of school-based substance use prevention programs in Low-Middle-Income-Countries (LMICs) to identify the underlying theories, models, or frameworks (TMF) and core components of school-based substance use prevention interventions among young people in LMICs. Findings indicate that while a range of TMFs are used in designing interventions, the most widely used TMF was social learning theory followed by theory of planned behaviour. Six core intervention components were identified: education, school environment, school policy, parental involvement, peer engagement and counselling. Phase two was a mixed-methods social networks study to describe the social network features and substance use prevalence of senior high school students in Ghana, with a focus on levels of homophily (with respect to gender and substance use), and possible mechanisms through which friendship networks influence young people’s substance use behaviour. The results indicate that gender identity is a strong predictor of friend selection. In terms of substance use, a key finding was that only a few young people indicated a preference for friends with same substance use behaviour. Taken together, quantitative and qualitative results identified the existence and importance of a “friendship network and gender norms” in determining substance use behaviour within friendship networks.In phase three, a deliberative dialogue with interest holders was organized to garner feedback on study findings, and recommendations regarding key considerations in designing school-based interventions for substance use prevention. I used findings from this phase, together with further review of the literature, to develop the School-based Substance Use Prevention (SSUP) framework. The SSUP framework has five main priorities: (i) the guiding principles of SSUP prevention interventions; (ii) the core components to consider when designing an SSUP intervention; (iii) delivery groups to prioritize in an SSUP intervention; (iv) SSUP intervention stakeholder engagement process; and (v) the application of TMFs in an SSUP intervention. While this project focuses on Ghana, its findings and the resulting SSUP framework may be applicable to other African settings experiencing a similar substance use burden among young people
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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 | 0,012 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
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 ».