Investigating language barriers in psychiatric care in Ghana
Bibliographic record
Abstract
Background: In psychiatric care, doctors depend on information from interactions with patients to arrive at the right diagnosis and appropriate therapy (Brisset C, Leanza Y, Rosenberg E, Vissandjée B, Kirmayer LJ, Muckle G, et al. language barriers in mental health care: a survey of primary care practitioners. J Immigr Minor Health. 2014;16(6):1238–46). This is a qualitative descriptive study which seeks to investigate language barriers faced by psychiatrists in their interactions with patients and the effectiveness of the strategies which are employed to deal with the barriers.Methods: Research questions are informed by Convergence (Giles H. Communication Accommodation Theory. In: Baxter LA, Braithewaite DO, editors. Engaging theories in interpersonal communication: multiple perspectives. Thousand Oaks ( CA): Sage Publications; 2008. p. 161–73). Using semi-structured interviews, psychiatric doctors from the three state-owned psychiatric hospitals in Ghana report their experiences with convergence strategies they use, and offer suggestions that could inform future studies and policy formulation with regard to psychiatric care in Ghana. A qualitative conventional content analysis was applied whereby coding categories were derived straight from the text data.Results: The literature suggests that the study is novel with reference to Ghana. Similar studies (Lee E. Cross-cultural communication: therapeutic use of interpreters in mental health for professionals and clinicians. In: Lee E, editor. Working with Asian Americans: a guide for clinicians. New York (NY): The Guilford Press; 1997; Swartz L, Drennan G. Beyond Words: notes on the ‘irrelevance’ of language to mental health services in South Africa. Transcult Psychiatry. 2000;37(2):185–201; Jarvis GE, Ahmed R, Ryder A, Kirmayer LJ. Assessing language barriers to mental health services in a multiethnic population: preliminary findings. Presentation at Health care Access for Linguistic Minorities: Breaking the Barriers. Montreal, Quebec, Canada; 2014) corroborate this paper’s findings that language barriers have the potential of causing misdiagnoses if psychiatrists do not employ effective communication strategies.Conclusions: The paper recommends areas for further research and improvement measures which carry policy implications.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".