A cross-sectional assessment of diabetes self-management, education and support needs of Syrian refugee patients living with diabetes in Bekaa Valley Lebanon
Notice bibliographique
Résumé
BACKGROUND: Patients with diabetes require knowledge and skills to self-manage their disease, a challenging aspect of treatment that is difficult to address in humanitarian settings. Due to the lack of literature and experience regarding diabetes self-management, education and support (DSMES) in refugee populations, Medecins Sans Frontieres (MSF) undertook a DSMES survey in a cohort of diabetes patients seen in their primary health care program in Lebanon. METHODS: Structured interviews were conducted with diabetes patients in three primary care clinics between January and February 2015. Scores (0-10) were calculated to measure diabetes core knowledge in each patient (the DSMES score). Awareness of long-term complications and educational preferences were also assessed. Analyses were conducted using Stata software, version 14.1 (StataCorp). Simple and multiple linear regression models were used to determine associations between various patient factors and the DSMES Score. RESULTS: A total of 292 patients were surveyed. Of these, 92% had type 2 diabetes and most (70%) had been diagnosed prior to the Syrian conflict. The mean DSMES score was 6/10. Having secondary education, previous diabetes education, a 'diabetes confidant', and insulin use were each associated with a higher DSMES Score. Lower scores were significantly more likely to be seen in participants with increasing age and in patients who were diagnosed during the Syrian conflict. Long-term complications of diabetes most commonly known by patients were vision related complications (68% of patients), foot ulcers (39%), and kidney failure (38%). When asked about the previous Ramadan, 56% of patients stated that they undertook a full fast, including patients with type 1 diabetes. Individual and group lessons were preferred by more patients than written, SMS, telephone or internet-based educational delivery models. CONCLUSIONS: DSMES should be patient and context appropriate. The variety and complexities of humanitarian settings provide particular challenges to its appropriate provision. Understanding patient baseline DSMES levels and needs provides a useful basis for humanitarian organizations seeking to provide diabetes care.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».