What works and what doesn’t: Gerontology focused PhD/ DSW graduates speak out
Bibliographic record
Abstract
Abstract Lin et al. (2015) projected there would be a shortage of approximately 195,000 social workers in the United States by 2030. In the next twenty years, it is estimated that Americans over the age of 65 will actually outnumber children under the age of 18 (US Census, 2018). With a longstanding reputation for being less “glamorous”, social work with older adults will continue to experience deficits in the amount of those who commit to this field of practice unless more lasting change occurs (Cummings et al., p. 645, 2005). We must take a closer look at what takes place in the classroom at schools of social work to understand why social workers are not interested in working with older adults (Scharlach et al., 2000). Berkman et al. (2016) described in their work that a critical shortage of gerontology-focused social work faculty exists in schools of social work. Thus, we cannot expect more social workers to work with older adults unless they are exposed to this work in their educational programs. The purpose of this study is to report on the academic experience, research agenda, professional experiences (practice and teaching), and future goals of social work PhD/ DSW graduates. Ten social work doctoral graduates were interviewed in order to understand the impact their academic programs had on their commitment to older adults in their field and to learn their recommendations for schools of social work in an effort to sustain and grow the gerontological workforce.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".