What do we know about the needs and challenges of health systems? A scoping review of the international literature
Notice bibliographique
Résumé
BACKGROUND: While there is an extensive literature on Health System (HS) strengthening and on the performance of specific HSs, there are few exhaustive syntheses of the challenges HSs are facing worldwide. This paper reports the findings of a scoping review aiming to classify the challenges of HSs investigated in the scientific literature. Specifically, it determines the kind of research conducted on HS challenges, where it was performed, in which health sectors and on which populations. It also identifies the types of challenge described the most and how they varied across countries. METHODS: We searched 8 databases to identify scientific papers published in English, French and Italian between January 2000 and April 2016 that addressed HS needs and challenges. The challenges reported in the articles were classified using van Olmen et al.'s dynamic HS framework. Countries were classified using the Human Development Index (HDI). Our analyses relied on descriptive statistics and qualitative content analysis. RESULTS: 292 articles were included in our scoping review. 33.6% of these articles were empirical studies and 60.1% were specific to countries falling within the very high HDI category, in particular the United States. The most frequently researched sectors were mental health (41%), infectious diseases (12%) and primary care (11%). The most frequently studied target populations included elderly people (23%), people living in remote or poor areas (21%), visible or ethnic minorities (15%), and children and adolescents (15%). The most frequently reported challenges related to human resources (22%), leadership and governance (21%) and health service delivery (24%). While health service delivery challenges were more often examined in countries within the very high HDI category, human resources challenges attracted more attention within the low HDI category. CONCLUSIONS: This scoping review provides a quantitative description of the available evidence on HS challenges and a qualitative exploration of the dynamic relationships that HS components entertain. While health services research is increasingly concerned about the way HSs can adopt innovations, little is known about the system-level challenges that innovations should address in the first place. Within this perspective, four key lessons are drawn as well as three knowledge gaps.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».