A Systematic Review of Effective Intercultural Communication in Mental Health
Bibliographic record
Abstract
Purpose of the Research: Effective intercultural communication between patients and clinicians is essential for the delivery of high quality health care. To date, there has been no systematic assessment of empirical literature on effective communication within a mental health context, despite theoretical models purporting certain criteria make health communications more effective. The current research aims to determine factors which aid or hinder communication effectiveness in such a context, and to see whether the literature supports Teal and Street’s (2009) theoretical model specifically. Additionally, the review considered the role of language disparity between patients and clinicians within the mental health context. Method: Electronic databases Medline, Psych Info and Web of Science were searched using an optomised search strategy in June 2014. Studies were considered where intercultural communication between a clinician and patient was a primary focus. To meet inclusion the papers needed to be empirical in nature, written in English, peer-reviewed and of sound quality. Data relating to the study type and analysis used, characteristics of participants (both patients and clinicians), cultural and communication information were extracted. Results: Eight papers were included in the final review. The results largely support Teal and Street’s model and highlight that nonverbal and verbal behaviour skill, recognition of cultural differences, incorporating cultural knowledge, alongside negotiation and collaboration with patients and family members, all aid in effective communication. Additionally, language barriers were found to pose a significant barrier to communication. The quantity of current literature investigating the effectiveness of intercultural communication specifically within a mental health setting is limited, highlighting the need for further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".