Social Work Interest in Prevention: A Content Analysis of the Professional Literature
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.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".