Cultural competence & derivatives in substance use treatment for migrants and ethnic minorities (MEM) : the discordance between systemic disparities and intervention level solutions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,069 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».