Social Work Interest in Prevention: A Content Analysis of the Professional Literature
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
Every day in the United States, over halfa million social workers provide services to people with health, mental health, and substance abuse problems in a fragmented system that emphasizes disease treatment over prevention. Powerful issues--including health inequities, population aging, globalization, natural disaster, war, and economic downturn--make the need for preventive approaches more critical than ever. Despite social work's historic commitment to enhancing human well-being and public health involvement, little is known about how social work currently views prevention or whether it is being addressed in the social work professional literature. To determine whether, and to what extent, prevention is addressed, discussed, and published in social work journals, the authors--all public health social work researchers-undertook a content analysis of nine peer-reviewed journals, analyzing all articles published from 2000 to 2005. A total of 1,951 articles were reviewed and coded for prevention according to specified criteria. A relatively small number--109 (5.6 percent)--were found to meet the criteria for being a prevention article, suggesting that prevention is still a minority interest area within social work.A renewed conversation about prevention in social work can enhance opportunities for strong social work participation in the transdisciplinary collaboration needed in this new era of health reform.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.028 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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