Preparing Social Work Practitioners to Use Evidence-Based Practice
Bibliographic record
Abstract
The implementation of evidence-based practice (EBP) as a professional model of practice for social work has been suggested as one approach to support informed clinical decision making. However, different barriers and processes have been identified that impact the use of EBP at individual, organizational, and systemic levels. This article describes results from a project that sought to enhance practitioner use of EBP by using a supportive strategy including training and technical assistance through a partnership between university-based researchers and three social work agencies. Results compare similarities and differences across each of the three agencies in terms of barriers and promoters at the team, organizational, and system levels. Results suggest that comprehensive multilevel interventions are needed to support the use of EBP in social work organizations and that further research is needed to test explicit partnership components. Findings suggest that a multilevel approach has the greatest potential to support implementation of EBP in social agencies.
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 | 0.263 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".