Development and Validation of a Culturally Competent Care Module (CCCM) and Its Efficacy on Nurses’ Cultural Competence and Patient Satisfaction: A Randomized Controlled Trial in a Tertiary Care Hospital in India. (Preprint)
Notice bibliographique
Résumé
BACKGROUND: Culturally responsive nursing involves delivering care that respects and reflects patients' cultural values, communication patterns, and belief systems. In the Indian context, increasing rural-to-urban migration along with the expansion of medical tourism, estimated to have attracted 2.1 million international patients in 2024, has intensified the need for culturally competent nursing care. Although educational interventions in this area have shown encouraging results, there remains a lack of well-designed randomized controlled trials (RCTs) in India. For the purpose of this study, structured training is understood as a planned, session-based approach to learning that includes activities such as simulation exercises, case-based discussions, and guided reflection. OBJECTIVE: This study aims to develop, validate, and evaluate a culturally competent care module (CCCM) through a 3-phase RCT, assessing its effects on nurses' cultural competence and patient satisfaction. METHODS: This 3-phase parallel-group RCT is being conducted in the medical-surgical wards of the Acharya Vinoba Bhave Rural Hospital, Wardha, Maharashtra, India (2024-2026). In phase 1 (completed), the CCCM was developed via thematic analysis of 20 interviews with nurses, educators, and patients. In phase 2 (completed), the CCCM and a patient satisfaction tool were validated using a 2-round Delphi process involving 15 experts (CCCM: content validity index ≥0.89; patient satisfaction tool: Cronbach α=0.87-0.92). Phase 3 is ongoing: 55 nurses are randomized in a 1:1 ratio to either the CCCM (5×1-hour sessions+boosters) or routine care. The primary outcome is the postintervention Cultural Competence Assessment Tool for Nurses score, while secondary outcomes include subscales, patient satisfaction, and 90-day retention. The sample size is calculated using G*Power based on an anticipated effect size of 0.82. Participants are randomly allocated through computer-generated block randomization stratified by seniority, and outcome assessors and data analysts are blinded to group allocation. Data will be analyzed using SPSS (intention-to-treat, linear mixed-effects models; P<.05). Patient satisfaction outcomes will be evaluated using data from 120 patients. RESULTS: Phases 1 and 2 were completed in July 2025. In phase 3, 110 (100%) nurses were successfully enrolled and randomized, with 55 participants assigned to each group. The intervention was delivered as planned, and postintervention data collection reached 70% completion. Additionally, of the planned 120 patients, 92 (77%) were enrolled for the patient satisfaction assessment. Full data analysis is expected to be completed by June 2026. CONCLUSIONS: This trial will provide evidence on the CCCM's noninferiority or superiority to routine care for enhancing cultural competence and satisfaction, supporting scalable training amid India's demographic shifts. TRIAL REGISTRATION: Clinical Trials Registry-India (CTRI) CTRI/2025/06/088982; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MTMzMjQ5&Enc=&userName=. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84015.
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 | 0,012 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».