Advancing Health Equity: A Research Review on Culturally Inclusive Healthcare Models for Marginalised Populations
Bibliographic record
Abstract
Culturally inclusive healthcare is essential for addressing health disparities and achieving global health equity. This comprehensive analysis explores access-oriented and delivery-oriented models, emphasizing the integration of cultural competence into healthcare systems. Case studies from diverse regions, including community health centres in Canada, the United States of America, India, Finland and the Family Health Program in Brazil, demonstrate the effectiveness of culturally tailored interventions in improving health outcomes. Innovations such as AI-driven personalization, telehealth, and virtual reality training for healthcare providers highlight the role of technology in bridging cultural and linguistic gaps. Despite progress, challenges persist, including resource constraints, systemic biases, and gaps in research on intersectionality and culturally tailored precision health. Recommendations include mandatory cultural competence training, enhanced community engagement, and policy reforms to support inclusive care. The future of culturally inclusive healthcare lies in leveraging technology, fostering global collaboration, and ensuring sustainability. By addressing these priorities, healthcare systems can advance equity and deliver care that respects and responds to the diverse needs of all individuals.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".