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Record W4327811463 · doi:10.1186/s12954-023-00740-x

A realist review of best practices and contextual factors enhancing treatment of opioid dependence in Indigenous contexts

2023· review· en· W4327811463 on OpenAlexafffund
Rita Henderson, Ashley McInnes, Ava Danyluk, Iskotoahka William Wadsworth, Bonnie Healy, Lindsay Crowshoe

Bibliographic record

VenueHarm Reduction Journal · 2023
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsConfederation CollegeCanadian Blood ServicesUniversity of Calgary
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsHealth psychologyPsychologyIndigenousInterpersonal communicationContextualizationOpioid use disorderOperationalizationHealth careApplied psychologySocial psychologyMedicineNursingPublic healthPolitical scienceOpioidComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this study was to examine international literature to identify best practices for treatment of opioid dependence in Indigenous contexts. METHODS: We utilized a systematic search to identify relevant literature. The literature was analysed using a realist review methodology supported by a two-step knowledge contextualization process, including a Knowledge Holders Gathering to initiate the literature search and analysis, and five consensus-building meetings to focus and synthesize relevant findings. A realist review methodology incorporates an analysis of the complex contextual factors in treatment by identifying program mechanisms, namely how and why different programs are effective in different contexts. RESULTS: A total of 27 sources were identified that met inclusion criteria. Contextual factors contributing to opioid dependence described in the literature often included discussions of a complex interaction of social determinants of health in the sampled community. Twenty-four articles provided evidence of the importance of compassion in treatment. Compassion was evidenced primarily at the individual level, in interpersonal relationships based on nonjudgmental care and respect for the client, as well as in more holistic treatment programs beyond biophysical supports such as medically assisted treatment. Compassion was also shown to be important at the structural level in harm reduction policies. Twenty-five articles provided evidence of the importance of client self-determination in treatment programs. Client self-determination was evidenced primarily at the structural level, in community-based programs and collaborative partnerships based in trust and meaningful engagement but was also shown to be important at the individual level in client-directed care. Identified outcomes moved beyond a reduction in opioid use to include holistic health and wellness goals, such as improved life skills, self-esteem, feelings of safety, and healing at the individual level. Community-level outcomes were also identified, including more families kept intact, reduction in drug-related medical evacuations, criminal charges and child protection cases, and an increase in school attendance, cleanliness, and community spirit. CONCLUSIONS: The findings from this realist review indicate compassion and self-determination as key program mechanisms that can support outcomes beyond reduced incidence of substance use to include mitigating systemic health inequities and addressing social determinants of health in Indigenous communities, ultimately healing the whole human being.

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 imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0140.010
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.127
GPT teacher head0.437
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2023
Admission routes2
Has abstractyes

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