Exploring the Experiences and Needs of Patients With Type 2 Diabetes Mellitus in Sleman Regency, Yogyakarta, Indonesia: Protocol for a Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic disease that can cause adverse effects if not managed effectively. The prevalence of T2DM will continue to rise every year, and data from the International Diabetes Federation show that the number of patients diagnosed with T2DM in Indonesia is predicted to increase from 10.3 million in 2017 to 16.7 million in 2045. Managing T2DM properly is a challenge for the patients because they need to implement lifestyle changes that involve the self-monitoring of blood glucose, consuming prescribed medication properly, maintaining a healthy diet, getting sufficient physical training, keeping a healthy sleeping pattern, managing stress properly, and consulting medical professionals regularly. The worldwide intervention for T2DM focuses on self-management education. The varied results in studies about interventions show that no particular intervention method can be regarded as the most effective. In Indonesia, there are limited studies on educational interventions to improve the quality of life and health of patients with T2DM. OBJECTIVE: This study aims to explore the experiences and needs of patients with T2DM in Sleman Regency, Yogyakarta, Indonesia, to develop effective self-management education. METHODS: The study will use the phenomenology method with purposive sampling to collect data. The inclusion criteria are patients in the Chronic Disease Self-Management Program at the Sleman Regency Public Health Center who are aged ≥18 years, diagnosed with T2DM for more than a year, with hemoglobin A1c levels ≤7.5% and >7.5%, capable of communicating verbally and literate in the Indonesian language, not deaf, and willing to participate. The data collection is based on the Social Cognitive Theory, which involves selecting assessment targets and analyzing personal factors, environment, and behavior that determine the knowledge, attitude, and adherence of persons with T2DM. Researchers will collect the data through in-depth, face-to-face interviews to learn about knowledge, self-efficacy, outcome expectancy, outcome experience, worry, illness belief, treatment belief, diet, physical activity, medicine intake, treatment pattern, support system, as well as ethnic and cultural influences. The results will be taken from unstructured and open-ended questions written in Indonesian according to the interview guidelines. The data analysis process will go through several stages: reading the data thoroughly; coding; sorting the categories; creating the themes; making general descriptions; and presenting the data in charts, narratives, and recorded quotations from the interviews. RESULTS: This study received a grant in May 2021 and gained permission from the Medical and Health Research Ethics Committee of Universitas Gadjah Mada, Indonesia, on July 1, 2021. Data collection started on August 12, 2021, and the results are expected to be published in 2022. CONCLUSIONS: The results of this study will be used to design an educational intervention model to improve the knowledge, attitude, and adherence of patients with T2DM. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37528.
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 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 | 0,020 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,003 |
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 ».