Knowledge Diffusion in Social Work: A New Approach to Bridging the Gap
Bibliographic record
Abstract
The continuing gap between research and practice has long been a problem in social work. A great deal of the empirical practice literature has emphasized practice evaluation (usually in the form of single-case methodologies) at the expense of research dissemination and utilization. An alternative focus for social work researchers can be found in the extensive theoretical and research literature on knowledge diffusion, technology transfer, and social marketing. Knowledge diffusion and social marketing theory is explored in terms of its relevance to social work education and practice, including a consideration of issues of culture and power. The authors present an integrated dissemination model for social work and use a case example to illustrate the practical application of the model. The OPTIONS (OutPatient Treatment In ONtario Services) project is an example of the effective dissemination of two research-based addiction treatment modalities to nearly 1,000 direct practice clinicians in Ontario, Canada.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".