Barriers and facilitators to the implementation of brief interventions targeting smoking, nutrition, and physical activity for indigenous populations: a narrative review
Bibliographic record
Abstract
OBJECTIVE: This narrative review aimed to identify and categorize the barriers and facilitators to the provision of brief intervention and behavioral change programs that target several risk behaviors among the Indigenous populations of Australia, Canada, and New Zealand. METHODS: A systematic database search was conducted of six databases including PubMeD, Embase, CINAHL, HealthStar, PsycINFO, and Web of Science. Thematic analysis was utilized to analyze qualitative data extracted from the included studies, and a narrative approach was employed to synthesize the common themes that emerged. The quality of studies was assessed in accordance with the Joanna Briggs Institute's guidelines and using the software SUMARI - The System for the Unified Management, Assessment and Review of Information. RESULTS: Nine studies were included. The studies were classified at three intervention levels: (1) individual-based brief interventions, (2) family-based interventions, and (3) community-based-interventions. Across the studies, selection of the intervention level was associated with Indigenous priorities and preferences, and approaches with Indigenous collaboration were supported. Barriers and facilitators were grouped under four major categories representing the common themes: (1) characteristics of design, development, and delivery, (2) patient/provider relationship, (3) environmental factors, and (4) organizational capacity and workplace-related factors. Several sub-themes also emerged under the above-mentioned categories including level of intervention, Indigenous leadership and participation, cultural appropriateness, social and economic barriers, and design elements. CONCLUSION: To improve the effectiveness of multiple health behavior change interventions among Indigenous populations, collaborative approaches that target different intervention levels are beneficial. Further research to bridge the knowledge gap in this topic will help to improve the quality of preventive health strategies to achieve better outcomes at all levels, and will improve intervention implementation from development and delivery fidelity, to acceptability and sustainability.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".