Community-Based Culturally Tailored Education Programs for Black Adults with Cardiovascular Disease, Diabetes, Hypertension, and Stroke: A Systematic Review Protocol
Bibliographic record
Abstract
Abstract Background Chronic conditions and stroke disproportionately affect Black adults in communities all around the world due patterns of systemic racism, disparities in care, and lack of resources. To address unequal care received by Black communities, a shift to community-based programs that deliver culturally-tailored programs to meet the needs of the communities they serve, including Black adults who tend to have reduced access to postacute services, may give an alternative to a healthcare model which reinforces health inequities. However, community-based culturally-tailored programs (CBCT) are relatively understudied but show promise to improve the delivery of services to marginalized communities. The objectives of this review are to: (i) determine key program characteristics and outcomes of CBCT programs that are designed to improve health outcomes in Black adults with cardiovascular disease, hypertension, diabetes, or stroke and (ii) identify which of the five categories of culturally appropriate programs from Kreuter and colleagues have been used to implement CBCT programs. Methods This is a protocol for a systematic review that will search MEDLINE, EMBASE, and CINAHL databases to identify community-based culturally-tailored programs for Black adults with cardiovascular disease, hypertension, diabetes, or stroke. Discussion Health inequities have disproportionately impacted Black communities and will continue to persist if adjustments are not prioritized within healthcare to provide services, care, and programs meant to address the specific barriers to better health experienced. Many interventions meant to improve the health outcomes of marginalized groups are created with little input from target communities, leading to interventions that may not address the specific barriers contributing to poor health outcomes and are designed and implemented from an outsider’s perspective. The inclusion of community members allows for a deeper understanding of the issues facing the community and provides an opportunity to incorporate cultural values to potentially increase the efficacy, tailoring the intervention to distinct communities. An alternative to current healthcare interventions must be explored to reduce the health gap experienced by Black adults. Trial registration PROSPERO CRD42021245772
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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 | 0.056 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".