[The use of psychodiagnostic tests in forensic psychiatric reports within the framework of internment. An exploratory study of records on internees in the judicial district of Ghent].
Bibliographic record
Abstract
BACKGROUND: In Belgium there is now a new law on the detention of mentally disordered offenders. The main problems with psychiatric reports compiled by experts concern the indistrict legal framework and the lack of financial resources; both of these factors may affect the quality of the reports. Earlier studies have shown that only a few standardised tests are used to substantiate the conclusions reached in the reports. AIM: To examine to what extent forensic psychiatrists use diagnostic tools to substantiate the conclusions in their reports. METHOD: We based our study on a sample consisting of 84 records of recent cases dealt with by the Committee for the Protection of Society (CPS) of the judicial district of Ghent. RESULTS: We found that diagnostic tools were used in 63% of the cases studied. CONCLUSION: In spite of the difficult circumstances in which a forensic psychiatrist has to work, test instruments were used regularly in diagnostics. Moreover, most of the test instruments used were considered acceptable according to the scientific literature. The study has shown the need for a clear-cut legal framework involving criteria that forensic psychiatrists must meet when conducting their research. Inspiration for these criteria is to be found in the Netherlands where psychiatrists are already working with a specific format for forensic psychiatric reports.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".