A Bridging Approach to Boost Doctoral Enrollment in a HBCU: An Exploratory Qualitative Study
Bibliographic record
Abstract
A declining enrollment in doctoral social work programs not only affects the sustainability of the programs, but also impacts the knowledge-based economy in the long run. The shortage of doctoral-prepared faculty, interwoven with the current national shortage of social workers, will limit effective service delivery, and generation of knowledge base for direct practice and policy advocates. Little is known about the barriers and strategies in Historically Black Colleges and Universities (HBCU). Two focus groups with in-depth interviews were conducted among Master of Social Work (MSW) students at a four-year public university. The study resulted in a wide range of identifiable strategies to boost the doctoral enrollment in the social work program, including more summer courses, more online courses, more flexible class time, higher integration of technology in the classroom and better curriculum structure. Findings suggest that non-traditional becomes the new traditional. With the advancement in smartphone and wireless technology, the University can reposition the program, seek for ways to serve the new traditional student population, and improve the infrastructure to accommodate students’ digital needs.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".