Translation and Validation of the Korean Version of the Global Interprofessional Therapeutic Communication Scale: A Study of the Psychometric Properties among Korean Nurses
Bibliographic record
Abstract
Purpose: This study aimed to validate the Korean version of the Global Interprofessional Therapeutic Communication Scale (K-GITCS), with the ultimate goal of improving therapeutic communication and patient engagement among Korean nurses.Methods: The study rigorously adhered to the original authors’ translation guidelines. A sample of 300 registered nurses from a tertiary hospital in South Korea participated in this research. Confirmatory factor analysis was conducted to verify the tool’s validity, and Cronbach’s ⍺ coefficients were calculated to evaluate the internal consistency of the K-GITCS.Results: The instrument’s reliability was substantiated by an adequate comparative fit index (0.984) and a high Cronbach’s ⍺ coefficient (0.94). The empirical results supported the three-factor structure of the K-GITCS, which comprised trust and rapport building, power sharing, and empathy.Conclusion: The study confirms that the K-GITCS is a valid, reliable, and culturally sensitive instrument for assessing therapeutic communication skills among nurses in Korea. It also highlights the importance of culturally tailored therapeutic communication training, particularly for promoting empathy in patient care. The study emphasizes the potential of the K-GITCS to significantly enhance nurses’ therapeutic communication practices, thereby improving the quality and safety of patient care. It is recommended to apply this tool among nursing students, academic institutions, and interprofessional healthcare providers to facilitate structured educational interventions that will improve therapeutic communication.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".