Social work fields of practice : historical trends, professional issues, and future opportunities
Bibliographic record
Abstract
Preface xiii About the Editors xv Contributions xvii Chapter 1. Child Welfare and Social Work Practice 1 Robin E. Perry and Alberta J. Ellett Chapter 2. Family-Centered Practice: A System-Based Approach to Strengthening Families 55 Barbara Thomlison and Raymond J. Thomlison Chapter 3. School Social Work 81 Cynthia Franklin, Beth Gerlach, and Amy Chanmugam Chapter 4. Substance Abuse 113 Lori K. Holleran Steiker and Samuel A. MacMaster Chapter 5. The Mental Health Field of Practice 147 King Davis and Hyejin Jung Chapter 6. Social Work Disability Practice 159 Elizabeth DePoy and Stephen Gilson Chapter 7. Geriatric Social Work: A Field of Practice 183 Roberta R. Greene Chapter 8. Forensic Social Work: Current and Emerging Developments 207 Katherine van Wormer, David W. Springer, and Tina Maschi Chapter 9. Veterinary Social Work Practice 245 Elizabeth Strand, Bethanie A. Poe, Sarina Lyall, Jan Yorke, Janelle Nimer, Erin Allen, Geneva Brown, and Teresa Nolen-Pratt Chapter 10. Social Work Practice With the Military 273 Anthony M. Hassan and Marilyn Flynn Chapter 11. International Social Work Practice 291 Doreen Elliott and Uma A. Segal Chapter 12. Immigrant and Indigenous Populations: Special Populations in Social Work 333 Jon Matsuoka and Hamilton I. McCubbin Chapter 13. Diversity Iris B. Carlton-LaNey and Sharon Warren Cook Author Index 385 Subject Index 397
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".