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Record W4399305573 · doi:10.1097/cxa.0000000000000207

Substance Use, Addiction and Support Services: Increased Risk and Service Inequity for Official Language Minority Communities in Canada

2024· article· en· W4399305573 on OpenAlexaffvenueabout
Kevin Prada, Danielle de Moissac

Bibliographic record

VenueThe Canadian Journal of Addiction · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversité de Saint-BonifaceMcGill University
Fundersnot available
KeywordsPopulationAddictionMental healthPolitical sciencePublic relationsService providerSubstance useHealth careMedicinePsychologyService (business)BusinessPsychiatryEnvironmental health

Abstract

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ABSTRACT Objectives: The increase in substance use and addictions within the Canadian population, illustrated by the current opioid crisis and exacerbated by the COVID-19 pandemic, urges the investigation of populations who may be particularly vulnerable to developing problematic substance use. Official language minority communities in Canada, both understudied and underserved, may be one such population. Methods: Through its tripartite design using scoping literature review, interview, and environmental scan methodologies, this study offers a snapshot of the current reality surrounding substance use, addictions, and related treatment services available to this linguistic minority population. Results: Results reveal significant gaps in knowledge and service provision for this population. Service provision and knowledge on this issue constitute a nationwide patchwork, leaving official language minority communities underserved. While enough is known to assert that their needs are different than those of their majority peers, much remains to be investigated, and efforts to ameliorate their social determinants of health, including cultural and linguistic competence on behalf of service providers, are urgently needed. Conclusions: Recommendations include networking, collaboration, financial support and active offer of services in both official languages to improve mental health and addictions services through a continuum of care. Systems navigators may facilitate promotion and referral to such services for official language minorities. Objectifs: La prévalence croissante de la consommation des substances et des dépendances au Canada, tel qu’illustrée par la crise actuelle des opioïdes et exacerbée par la pandémie de la COVID-19, exhorte à l’investigation de populations qui pourraient être particulièrement vulnérables à la consommation problématique de substances. Au Canada, les communautés de langue officielle en situation de minorité (CLOSM), une population à la fois peu étudiée et peu desservie, pourrait bien représenter une telle population. Méthodes: Au moyen de sa stratégie tripartite, employant les méthodologies d’étude de la portée, d’entrevues, et de scan environnemental, cette étude brosse un premier portrait de la réalité actuelle quant à la consommation de substances, aux dépendances et aux services qui y sont voués, parmi les CLOSM au Canada. Résultats: Les résultats révèlent une lacune importante aux niveaux des connaissances et des services desservant les CLOSM dans la majorité des provinces et territoires. Bien que l’état des lieux soit parcellaire en raison du manque d’études scientifiques sur la question, le portrait actuel suffit pour constater que les besoins des CLOSM différent de ceux de leurs pairs de langue majoritaire. Une amélioration des déterminants sociaux de la santé, tel la compétence culturelle et linguistique de la part des pourvoyeurs des services, représente un besoin pressant. Conclusions: Les recommandations, qui comprennent le réseautage, la collaboration, l’appui financier et l’offre active des services dans les deux langues officielles, visent à améliorer les services voués à la santé mentale et aux dépendances auprès des CLOSM au sein d’un continuum de soins. Les navigateurs.rices de systèmes pourraient faciliter l’aiguillage de membres des CLOSM vers ces services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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