Assessing Short-term, Dynamic Changes in Risk: The Predictive Validity of the Brockville Risk Checklist
Bibliographic record
Abstract
In the present study, we examined the predictive utility of the Brockville Risk Checklist (BRC), a structured assessment tool for clinical care planning, using a semi-parametric regression technique. We examined BRC scores and the frequency and type of incidents (aggression, noncompliance, etc.) over 13 assessments for 121 psychiatric patients at a medium-secure forensic unit. Most patients were male (95%), on average 40.9 ( SD = 13.0) years old, and diagnosed with a psychotic disorder (78%). Generalized estimating equation (GEE; Liang & Zeger, 1986) modeling was used in this study to determine if changes in dynamic risk scores over time predicted outcomes (presence or absence of an incident) during the approximately six-week follow-up period. Results showed that scores on the Harm to Others scale assessed at one case conference significantly predicted changes in aggressive and total incidents recorded in the subsequent case conference. The BRC shows promise as a dynamic measure of inpatient aggression, predicting verbal or physical incidents an average of six weeks later.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".