Providing Education and Training to Health Care Professionals to Address COVID-19 Health Disparities: Protocol for Implementation Project Using Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has underscored the need for targeted interventions to address health care disparities among specific health care professionals and mitigate the impact of the virus. In response, we developed a comprehensive statewide educational program protocol focused on subject areas of health equity, cultural sensitivity, infection prevention and control (IPC), and quality improvement (QI). OBJECTIVE: The project aims to improve health care professionals' knowledge and practice skills in the 4 subject areas, increase their comfort level in implementing health disparities-related QI projects, and facilitate the successful completion of QI projects addressing COVID-19 health disparities within their practice settings. METHODS: The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used in the planning and evaluation of this innovative educational program, which combines the Extension for Community Healthcare Outcomes (ECHO) learning model with one-on-one QI coaching. Participants engage in virtual interactive sessions led by experts and consultants, covering didactic presentations, case discussions, COVID-19 updates, and assessments. QI and health equity coaches provide guidance on developing QI projects targeting COVID-19 and other health disparities. Evaluation surveys are used for baseline, midpoint, and end-of-program assessment for self-reported comfort levels with knowledge and practice-based learning competencies in all 4 subject areas and health disparities-related QI project implementation. The Wilcoxon rank-sum test and Cochran-Armitage trend test will be used to compare pre- and postsurvey responses. Data from semistructured qualitative interviews, which capture insights into participants' application of ECHO training, will be analyzed using an inductive content analysis approach. RESULTS: A total of 50 ECHO sessions were held between November 2021 and May 2024. Overall, 510 participants attended at least one ECHO session, resulting in 3316 teaching encounters. The pre- and postsurvey data will be analyzed to study project impact and will be ready for publication in June 2026. CONCLUSIONS: By using implementation science methods, an innovative and comprehensive educational protocol was developed that integrates the training curriculum, evaluation metrics, and coaching support, allowing for the translation of the training into actionable community projects focused on addressing health disparities. This model has shown initial promise in terms of feasibility and uptake. Further studies are needed to evaluate the long-term effectiveness of these QI projects in reducing COVID-19 disparities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60901.
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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 | 0.153 | 0.134 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.013 |
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".