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)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".