Cultural competence & derivatives in substance use treatment for migrants and ethnic minorities (MEM) : the discordance between systemic disparities and intervention level solutions
Bibliographic record
Abstract
Introduction: Disparities in substance use treatment (SUT) among varying types of migrants and ethnic minorities (MEM) compared to non-MEM counterparts are documented extensively across the continents. These disparities regard access, referral, diagnosis, waiting times, retention rates and presence across the treatment spectrum. Since the turn of the century cultural competence (CC) gained influence in SUT theory and practice as an effective response to overcome these disparities. Nevertheless, it remains unclear how CC is related to these disparities. This paper aims at understanding (1) the nature and origin of CC in SUT, (2) the premises of CC argumentation in SUT, (3) how CC theory components are questioned, (4) to what degree CC SUT outcomes correspond to these premises and finally (5) what is left unquestioned in the CC literature. Methods: We conducted a literature review (2007-2017) focused on CC SUT aimed at MEM. The literature search located 41 meta-, narrative, systematic, conceptual, historical and other reviews of models and components of CC in SUT. We applied Bacchi’s “What’s the problem represented to be?” approach (2009) thus analysing problem representations, underpinning presumptions, how the former are questioned and what is left unquestioned. The identified CC presuppositions are categorised following an ecosocial perspective (micro, meso, macro). Results: Most of the identified studies build on Cross et.al.’s (1989) CC definition. (1) Northern American studies mainly describe individual and organisational CC as well as culturally adapted interventions. Scholars from mainly Australia, Canada and New-Zealand describe culture-based intervention strategies for indigenous populations. Little EU studies were located. (2) Presuppositions in arguing for CC and culture-based approaches are mainly located at the macro level (disparities in [mental] health, dominant [professional] cultures, increasing diversity in society and the right to health), meso and micro level argumentation are described extensively, but to a lesser degree. (3) Questioned issues are CC’s culturalising and stereotyping effects, the limitations of CC, ‘the universalist stance’ and CC conceptual vagueness. (4) Most outcome indicators focus on workforce (meso) and intervention (micro) and not the system level. (5) Unproblematised themes are high rates of incarceration of MEM groups, prevalence rates as presuppositions, the lack of accessibility to SUT as a component of CC, the lack of comparing provider to user perspectives in outcome studies and the use of prevention literature in arguing for CC in SUT. Discussion: CC relies largely on the underlying and often unproblematised assumptions of what ‘culture’ is. Also, many of its presuppositions are in line with social recovery movement argumentation. Although most studies that argue for CC SUT do so from a health inequality perspective only some identify how components of CC work to reduce these disparities. Future research should focus on how the identified components both individually and conjointly address specific SUT disparities, how they are applied in SUT practice and whether they are in line with SUT needs and perspectives of service users with varying MEM backgrounds. Lastly, the lack of monitoring and the proliferation of derived CC concepts in the EU should be further examined.
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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".