Afrocentric approaches to primary health care provision with Black populations: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify and map how the global literature describes Afrocentric approaches to primary health care provision for Black populations in both clinical and community settings to support culturally responsive care. INTRODUCTION: Most Black populations have roots in Africa, and there are specific sociocultural characteristics unique to African contexts. For Black populations, an Afrocentric approach to health is valuable because it can challenge anti-Black racism and promote health equity. Therefore, the consideration or acknowledgment of these unique aspects is vital for Black populations. ELIGIBILITY CRITERIA: Global literature on Afrocentric approaches to primary health care provision with Black populations will be included. To provide context for this review, we are using our evolving definition of Afrocentric approaches to primary health care provision as follows: culturally meaningful patient engagement processes; clinical practices; and procedures that are grounded in the values, worldviews, and lived experiences of Black peoples of African descent. We will include approaches used for all Black populations. METHODS: This review will be conducted using the JBI scoping review methodology. We will search 6 academic databases and include qualitative, quantitative, and mixed methods study designs, with no date limitations. The gray literature search will include opinion, policy, and practice documents from specific health organization websites. Two reviewers will independently complete the title and abstract screening, followed by full-text review and data extraction. Articles published in English will be included, with other languages included if English translations are available. English translations will be requested from journal authors. REVIEW REGISTRATION: OSF https://osf.io/e2vxq/overview.
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 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 | 0.159 | 0.116 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.018 |
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".