Assessing normative approaches to communicating violence risk: a national survey of psychologists
Bibliographic record
Abstract
There is growing attention to the importance of violence risk communication, and emerging empirical evidence of how evaluating clinicians who conduct risk assessments communicate their conclusions about the risk of violence toward others. The present study addressed the perceived value of different forms of risk communication through a national survey of practicing psychologists (N = 1,000). Responses were received from a total of 256 participants, who responded to eight vignettes in which three factors relevant to risk communication were systematically varied in a 2 x 2 x 2 within-subjects design, counterbalanced for order: (i) risk model (prediction oriented versus management oriented), (ii) risk level (high risk versus low risk), and (iii) risk factors (static versus dynamic). Participants were asked to rate the value of six styles of risk communication for each of eight vignettes. The most highly valued style of risk communication involved identifying risk factors applicable to the individual, and specifying interventions to reduce risk. These results were consistent with findings from several previous studies in this area, and reflect an emerging trend in preferences for style and context of risk communication of violence.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".