Interventions, best practices, and programs for addressing the social determinants of substance use by Canadian First Nations females: a scoping review protocol
Bibliographic record
Abstract
Introduction: The Canadian Aboriginal population, encompassing at large First Nation, Metis, and Inuit (FNMI) communities, is at greater risk of substance use and substance use disorder than their non-Aboriginal counterparts. The legacy of oppression under colonialism has yielded poorer health outcomes in tandem with impoverished social determinants of health, such as socioeconomic status, housing conditions, employment, education, health literacy and access to healthcare, that persist to present day. Significantly, this disparity is preserved in female versus male Canadians, with Canadian Aboriginal females at greatest risk for disadvantage. Specifically, for these individuals, there remains a dearth of cohesive appraisal of programs addressing social risk factors for substance use disorder. This scoping review has been designed to explore this gap by evaluating what evidence-based practices and community programs can address specific social determinants of substance use disorder. In addition to collating which social determinants have been addressed to reduce substance use disorder development in FNMI females, we aim to evaluate what social determinants have been addressed at a deficit and where there remains a gap in the continuum of care for substance use disorder in this population. Methodology and analysis: This scoping review has been designed according to the JBI scoping review methodology delineated in the Joanna Briggs Institute Methods Manual for scoping reviews. Determinants were extrapolated through the World Health Organization’s (WHO) Conceptual Framework for Action on the Social Determinants of Health, and Health Canada’s “Honouring Our Strengths” framework. The search strategy will employ the following databases: PsychINFO, Embase, PubMed, Web of Science, and Ovid Global Health. Two independent reviewers will screen for eligible studies and conduct data abstraction and analysis. Grey literature will be included for comprehensiveness. Ethics and dissemination: Data analysis will be secondary to published content and ethics approval is not necessary. Results will be disseminated through a peer-reviewed journal and conferences. The goal is to assist researchers, academics, and policy makers for future informed action aiming to reduce substance use disorder in FNMI females.
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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 | 0.095 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".