Examining global Indigenous community wellness worker models: a rapid review
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in employing community wellness worker models in Indigenous populations to address inequities in healthcare access and outcomes, concerns about shortage in health and mental health human resources, and escalating burden of chronic and complex diseases driving significant increase in health services demand and costs. A thorough review of Indigenous community wellness worker models has yet to be conducted. This rapid review sought to outline the characteristics of a community wellness worker model in Indigenous contexts across the globe, detailing factors shaping implementation challenges and success. METHODS: A rapid review of the international peer-reviewed and grey literature of OVID Medline, Global Index Medicus, Google, and Google Scholar was conducted from January to June 2022 for Indigenous community wellness/mental health worker models and comparative models. Articles were screened and assessed for eligibility. From eligible articles, data pertaining to study design and sample; description of the program, service, or intervention; model development and implementation; terminology used to describe workers; training features; job roles; funding considerations; facilitators and barriers to success; key findings; outcomes measured; and models or frameworks utilized were extracted. Data were synthesized by descriptive and pattern coding. RESULTS: Twenty academic and eight grey literature articles were examined. Our findings resulted in four overarching and interconnected themes: (1) worker roles and responsibilities; (2) worker training, education, and experience; (3) decolonized approaches; and (4) structural supports. CONCLUSION: Community wellness worker models present a promising means to begin to address the disproportionately elevated demand for mental wellness support in Indigenous communities worldwide. This model of care acts as a critical link between Indigenous communities and mainstream health and social service providers and workers fulfill distinctive roles in delivering heightened mental wellness supports to community members by leveraging strong ties to community and knowledge of Indigenous culture. They employ innovative structural solutions to bolster their efficacy and cultivate positive outcomes for service delivery and mental wellness. Barriers to the success of community wellness worker models endure, including power imbalances, lack of role clarity, lack of recognition, mental wellness needs of workers and Indigenous communities, and more.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".