Exploring the identification, validation, and categorization of the cost and benefits of criminal justice in mental health: the PECUNIA project
Bibliographic record
Abstract
BACKGROUND: Mental health disorders and their treatments produce significant costs and benefits in both healthcare and non-healthcare sectors. The latter are often referred to as intersectoral costs and benefits (ICBs). Little is known about healthcare-related ICBs in the criminal justice sector and how to include these in health economics research. OBJECTIVES: The triple aim of this study is (i) to identify healthcare-related ICBs in the criminal justice sector, (ii) to validate the list of healthcare-related ICBs in the criminal justice sector on a European level by sector-specific experts, and (iii) to classify the identified ICBs. METHODS: A scientific literature search in PubMed and an additional grey literature search, carried out in six European countries, were used to retrieve ICBs. In order to validate the international applicability of the ICBs, a survey was conducted with an international group of experts from the criminal justice sector. The list of criminal justice ICBs was categorized according to the PECUNIA conceptual framework. RESULTS: The full-text analysis of forty-five peer-reviewed journal articles and eleven grey literature sources resulted in a draft list of items. Input from the expert survey resulted in a final list of fourteen unique criminal justice ICBs, categorized according to the care atom. CONCLUSION: This study laid further foundations for the inclusion of important societal costs of mental health-related interventions within the criminal justice sector. More research is needed to facilitate the further and increased inclusion of ICBs in health economics research.
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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 | 0.100 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".