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.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".