Forensic Case Formulation
Bibliographic record
Abstract
Formulation is the process or product of gathering and integrating diverse information to develop a concise account of the nature and etiology of the problems affecting a person's mental health to guide idiographic treatment design and other decision-making. Formulation is a core competency in mental health practice, including forensic mental health; however, there is no agreement concerning the details of how it should be done or how to evaluate it, either generally or more specifically with respect to forensic mental health. The purpose of this paper is to raise specific issues in the practice and evaluation of forensic case formulation, and so enhance the profile of this important area of work. In this paper, we (1) define case formulation and describe its key features, (2) specify criteria for evaluating case formulation, and (3) address challenges in forensic practice, with suggestions for advancing practice through research. We conclude with a proposed research agenda that we hope will encourage and promote activity in this important area.
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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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 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".