CLINICAL APPROACHES TO CULTURAL DIVERSITY IN MENTAL HEALTH CARE. A LITERATURE REVIEW.
Bibliographic record
Abstract
Background and Aims: Inequity on mental healthcare access is a main problem for migrants. In addition, migration-related factors may function as social determinants for mental health. It is necessary to adapt our services to the needs of this vulnerable population. This should be done taking into account that cultural factors have an impact on the way that mental illness and treatment are conceptualized.The aim of this study is to gain a global vision of the different types of clinical approaches to migrantsu2019 mental healthcare that have been developed throughout the world.Methods: Literature review via scientific database (PubMed, PsycInfo) was conducted. The screening process resulted in a selection of 32 papers.Results: The adaptations to cultural diversity in mental health are mostly found in England, France, Canada, Australia and the US. In recent years, they have started spreading to other countries. Some of the adaptations found were: using interpreters and cultural brokers, professionals training and supervision, innovations on the therapeutical setting, cultural consultation and ethno-specific clinics. How these adaptations are developed is related to the country's migration patterns, as well as its citizenship model.Conclusions: There are many ways in which we can improve mental healthcare access for migrants. However, there is still a long way to get away from ethnocentric perspectives, treasuring multicultural pluralism
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".