Building capacity for health research in higher education: Evaluating readiness for research and scholarship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This case study examines key components of research readiness for Doctoral Universities with High Research Activity (R2) through the lens of the National Institutes of Health (NIH) funded Research Centers in Minority Institutions (RCMI) program. While there are long-standing systems in place for ranking research institutions based on grant expenditures, there is a need to establish methods to evaluate institutions' readiness for externally funded competitive research. Drawing on literature across multiple disciplines and framed in alignment with the RCMI program goals, the authors developed a multidimensional instrument to examine research readiness centered on two primary domains (investigator and institutional readiness). The largest barriers to proposal development and conducting research/scholarship projects were managing time for competing expectations including scholarship, teaching, mentoring students, and service and recruiting skilled students who are prepared to assist the research process (respectively). Building research capacity requires research infrastructure, equitable distribution of institutional and grant resources, protected time for research, and focused areas research. Findings inform evaluation methods to assess research readiness, identify indicators and set benchmarks to measure changes in capacity and offer strategies for universities to enhance research capacity-building efforts and optimize institutions' ability to conduct NIH mission relevant research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it