Is Knowledge Contagious? Diffusion of Violence-Risk-Reporting Practices Across Clinicians’ Professional Networks
Bibliographic record
Abstract
The knowledge–practice gap remains a challenge in many fields. Health research has shown that professional networks influence various aspects of patient care, including diffusion of innovative practices. In the current study, we examined the potential utility of professional networks to spread the use of violence-risk-assessment tools in forensic psychiatric settings. A total of 6,664 reports, written by 708 clinicians, were used to examine the effect of clinicians’ use of risk-assessment tools on subsequent reports by other clinicians with whom they share patients. Results show that professional networks serve as an important channel for the spread of assessment practices. Simulation of a continuing education program showed that targeting more influential clinicians in the network could be 3 times more efficient at disseminating best practices than randomly training clinicians. Decision-makers may consider using professional networks to identify and train influential clinicians to maximize diffusion of the use of risk-assessment instruments.
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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 | 0.032 | 0.229 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".