Financial education for HIV‐vulnerable youth, orphans, and vulnerable children: A systematic review of outcome evidence
Notice bibliographique
Résumé
Problem: According to Joint United Nations Programme on HIV/AIDS (UNAIDS) and the World Health Organization, HIV is the leading cause of youth mortality in Africa, and the second cause of death among young people worldwide. Global commitments to reverse the HIV epidemic will only be achieved if strategies prioritize children and youth. Relevant evidence reviews found mixed evidence that HIV prevention may be addressed through economic strengthening activities such as financial education for youth. There was some evidence related to the potential for plural interventions that include both financial and sexual, reproductive health education. However, there is limited quality evidence that focused on HIV vulnerable youth in low- and middle-income countries (LMICs). Aims: This systematic review assessed the scope and strength of evidence for financial education and plural interventions aimed at reducing HIV vulnerability for youth, orphans and vulnerable children (OVC) in LMICs. Methods: Standard methodological procedures expected of systematic reviews were used. Six scientific and 24 grey literature sites were searched for relevant studies in English, French, Spanish and Arabic published between 1990 and 2016. Experimental and quasi-experimental research methods were considered where data was gathered at baseline and at least 6 months after the end of the intervention. Mixed-methods studies were considered provided they demonstrated validity in terms of randomization, appropriate sampling and controls, and minimization of bias errors and attrition. Evidence was then analysed and mapped to show types of financial and plural interventions by outcome type, direction and strength of evidence through qualitative assessments by the team. In addition, meta-analysis of odds ratios was conducted to validate the strength of evidence. This analysis illustrated the relative effect or weight of interventions on HIV-related outcomes based on confidence levels and sample sizes. Results: Of 5,216 records, 16 moderate to higher quality studies representing 10 interventions were identified, mostly focusing on HIV-vulnerable girls in Sub-Saharan Africa. More than half of the interventions were plural and included access to finance and counselling or supports to improve confidence, negotiating ability and social conditions. Most studies used an experimental design. Only 11 of the 16 studies had comparable enough measures to be validated with meta-analysis of odds ratios. Findings: The strongest evidence showed plural education interventions with self-efficacy supports, with and without savings to have positive effects on HIV-related outcomes. These outcomes included improved knowledge, attitudes and reduced sexual risk-taking behaviour. Evidence also showed improved self-efficacy from plural interventions, the changes in confidence, negotiating ability and social conditions that enable people to act on knowledge. Self-efficacy seems important as both a set of conditions to support reduced vulnerabilities and a way to measure them in terms of outcomes. While positive effects were also observed related to increased savings and improved attitudes toward saving, generally interventions showed mixed effects on financial and economic outcomes. Conclusions: This systematic review supports emerging evidence that plural interventions are associated with positive health and economic outcomes for vulnerable youth and children in LMIC. Even so, as a body of evidence, it is not clear which components are effective at producing favourable outcomes. Therefore, asset theories linking financial education and asset building with favourable outcomes for vulnerable youth are not as clear as may be commonly assumed. Quality evidence is needed in more settings separating out economic, health and self-efficacy components to better understand pathways and effects on outcomes. Segmentation in quantitative studies will enhance our understanding of asset, capability and self-efficacy theories for greater impact. Mixed methods and qualitative studies will be important complements to enhance our understanding of contextual conditions and how to build assets and self-efficacy in HIV vulnerable youth and OVC.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».