Why does transcultural consultation matter?
Bibliographic record
Abstract
Introduction Psychiatrists should be aware of the new challenges and needs that globalization poses. Objectives To highlight the need for a culturally sensitive approach to mental health. Methods Non-systematic review of “cultural consultation” on PubMed. Results Most people assume a direct connection between pathophysiology and clinical symptoms. However, evidence shows that the translation of pathophysiology and psychopathology into specific symptoms is mediated by cognitive processes and social interactions, which reflect models/practices specific to our culture. Patients focus on specific aspects of being sick, reinforced by cultural narratives or to fit expectations. Thus, people from different cultural backgrounds might have trouble communicating; cultural idioms of distress can be misinterpreted. The role of structural violence bestowed upon cultural minorities, which leads to discrimination and social exclusion, has extensively been studied as a risk factor for mental illness. Furthermore, ignoring cultural differences and diversity has been shown to contribute to healthcare disparities, hampering access to care and diminishing the quality of care received. In Canada, the Cultural Consultation Model provides cultural expertise, either by evaluating patients (preferably accompanied by the referring doctor, a translator and cultural mediator) for 1-3 sessions, providing recommendations to the referring doctor; or providing consulting to a referring doctor or organisation, through general guidance or discussion of specific cases. Conclusions In an evergrowing globalization process, we will inevitably have more contact with patients from culturally distinct backgrounds. To provide the best care, we must be aware of the ways in which culture can shape symptom expression, and take into account cultural explanations and preferences. Disclosure No significant relationships.
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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 | 0.069 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".