Protective Factors in Forensic Mental Health: A New Frontier
Bibliographic record
Abstract
The field of violence risk assessment has made substantial strides in the past four decades. In large part, these advances reflect the addition of purpose-designed risk assessment measures such as the HCR-20 and COVR as well as the contributions of prolific scholars and state of the art studies (Hodgins’ Aftercare Project; Monahan, Steadman et al.'s MacArthur Violence Risk Assessment study). However, important areas of inquiry have been left largely unexplored. The potential incremental value to be added by dynamic risk factors to historical and static factors is relatively unexamined. Yet, changeable factors offer the capacity to identify new opportunities for the prevention and management of violence risk. Similarly, the added value to be offered by a consideration of protective factors in addition to risk factors is only now emerging as a field of inquiry in adult forensic mental health. This special section is dedicated to addressing some of these limitations and provides papers describing two new measures (SAPROF and START) and empirical evidence supporting the role of protective factors in risk assessment and risk management research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".