Qualitative Assessment CIH Institutions’ Engagement With Underserved Communities to Enhance Healthcare Access and Utilization
Bibliographic record
Abstract
Background: In North America, there is a notable underutilization of complementary and integrative health approaches (CIH) among non-White and marginalized communities. Objectives: This study sought to understand how CIH educational instutitions are proactively working to redress this disparity in access and utilization among these communities. Methods: We conducted interviews with 26 key informants, including presidents, clinicians, and research deans across 13 CIH educational institutions across the US and Canada. Thematic analysis included deductive codes based on the interview guide during interview scripts review. Results: Six themes were identified: (1) CIH institutions often had a long and varied history of community engaged care through partnerships to increase access and utilization; (2) CIH institutions' long-standing community outreach had been intentionally designed; (3) CIH institutions provided an array of services to a wide range of demographics and communities; (4) addressing healthcare access and utilization through community partnerships had a strong positive impact; (5) funding, staffing and COVID-19 were significant challenges that impeded efforts to increase CIH access through community engaged work; (6) identified gaps in community partnerships and services to increase access and utilization were recognized. Conclusion: These findings underscore significant efforts made to enhance healthcare access and utilization among marginalized, underserved, and racial and ethnic communities. However, barriers such as funding constraints, resource allocation, and the need for proper measurement and accountability hinder proactive initiatives aimed at redressing disparities in CIH utilization within these communities.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".