The Health Equity Scholars Program: Fostering Culturally Competent and Successful Independent Investigators in Alzheimer's Disease and Related Dementia Research
Bibliographic record
Abstract
INTRODUCTION: The Health Equity Scholars Program (HESP) addresses the critical need for a diverse, culturally competent workforce to study and treat older adults from underrepresented populations (URPs) with Alzheimer's disease and related dementias (AD/ADRD). The HESP offers tailored mentored training in AD/ADRD research concepts, aiming to develop successful independent researchers. It recruits Scholars from underrepresented backgrounds as well as those passionate about AD/ADRD health equity research. METHODS: We (1) describe the fundamental elements of the HESP, and (2) present preliminary data from the HESP program evaluation results performed by an outside agency, pre-post participation surveys, and Scholar accomplishments. RESULTS: The HESP Scholars reported high rates of proficiency, satisfaction, and competency in nearly all evaluated areas, and have been successful in obtaining grants, promotions, and publications. DISCUSSION: These initial outcomes data suggest that the HESP is meeting its objective of diversifying the workforce in the field of AD/ADRD research and care. HIGHLIGHTS: The Health Equity Scholars Program aims to cultivate a diverse and culturally competent workforce, who are well-prepared to study and treat underrepresented older adults with Alzheimer's disease and related dementias (AD/ADRD). The program provides tailored mentored training in AD/ADRD research concepts, with the goal of nurturing successful independent researchers. Rigorous evaluation processes for applications ensure the selection of highly qualified Scholars. The program includes tailored training activities such as seminars and grant writing workshops, and tracks Scholar achievements while undergoing annual external evaluation to enhance its training program iteratively.
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 | 0.044 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".