User-experience and patient satisfaction with quality of tuberculosis care in India: A mixed-methods literature review
Notice bibliographique
Résumé
Tuberculosis affected 2.7 million people in India in 2017. The Revised National TB Control Programme has achieved milestones in coverage, however quality of TB care remains highly variable and often poor, with significant gaps in provider knowledge, practices, and patients consistently lost to follow-up. These quality gaps are largely informed by studies on provider practices or objective chart abstractions and case data. Per the knowledge of the author, no review has been conducted on first-hand patient perspectives on the quality of TB care they receive. This mixed-methods literature review aims to synthesize evidence on user-experience and patient satisfaction with TB care in India and inform areas for service quality improvement. Five medical databases, including PubMed, EMBASE, Global Health (Ovid), Web of Science, and CINAHL were searched for empirical studies on patient perspectives on TB health services published between January 1st, 2000 to December 31st, 2017. Studies in English with adult patients with any form of TB in the public or private health system were included. Studies prior to entering the health system, on distance to health facilities and cost were excluded. Seven Indian journals were hand searched and a grey literature search was conducted in GoogleScholar. Studies were assessed for methodological quality and thematic analysis was conducted by categorizing data using NVivo 12. A total of 498 studies were screened, of which 23 met the inclusion criteria. 16 supplementary studies were identified from Indian journals and grey literature. Of the 39 total studies included most were quantitative (29; 74%), based in South India (17; 44%) and focused on drug-sensitive TB patients (19; 49%) within the public health system (25; 64%). Data collection methods were highly heterogenous which limited synthesis and comparisons across population demographics, health sectors, or regions. Overall quantitative patient satisfaction measured in seven studies was high. Two major themes identified were provider-related factors (n = 26 studies) and convenience (n = 25), and six minor themes were supplies and equipment availability (n = 12), confidence (n = 10), information and communication (n = 10), waiting time (n = 8), stigma (n = 4), and confidentiality (n = 4). Each reported positive and negative user-experiences. Most significantly, DOTS did not fit the daily needs and obligations of many patients, particularly due to conflicts with employment and frequency of visits; while positive provider support, information, and flexibility helped patients adhere to treatment. Although quantitative patient satisfaction was found to be high, data were not collected using robust, validated tools. Qualitative and quantitative user-experiences in each theme were variable, making them both barriers and facilitators of good quality TB care. Poor user-experiences were often responsible for patients interrupting treatment or dropping out of TB care. Patient-centeredness, or user-friendliness of TB care can be improved by introducing individualized or flexible DOTS that is responsive to user circumstances and needs. User-experience data should be systematically collected using a standardized, national tool for identification of specific bottlenecks and successes in quality of TB care from the patients’ perspective.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 | 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 ».