PriSUD-Nordic—Diagnosing and Treating Substance Use Disorders in the Prison Population: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: A large proportion of the prison population experiences substance use disorders (SUDs), which are associated with poor physical and mental health, social marginalization, and economic disadvantage. Despite the global situation characterized by the incarceration of large numbers of people with SUD and the health problems associated with SUD, people in prison are underrepresented in public health research. OBJECTIVE: The overall objective of the PriSUD (Diagnosing and Treating Substance Use Disorders in Prison)-Nordic project is to develop new knowledge that will contribute to better mental and physical health, improved quality of life, and better life expectancies among people with SUD in prison. METHODS: PriSUD-Nordic is based on a multidisciplinary mixed method approach, including the methodological perspectives of both quantitative and qualitative methods. The qualitative part includes ethnographic fieldwork and semistructured interviews. The quantitative part is a registry-based cohort study including national registry data from Norway, Denmark, and Sweden. The national prison cohorts will comprise approximately 500,000 individuals and include all people imprisoned in Norway, Sweden, and Demark during the period from 2000 to 2019. The project will investigate the prison population during three different time periods: before imprisonment, during imprisonment, and after release. RESULTS: PriSUD-Nordic was funded by The Research Council of Norway in December 2019, and funding started in 2020. Data collection is ongoing and will be completed in the first quarter of 2022. Data will be analyzed in spring 2022 and the results will be disseminated in 2022-2023. The PriSUD-Nordic project has formal ethical approval related to all work packages. CONCLUSIONS: PriSUD-Nordic will be the first research project to investigate the epidemiology and the lived experiences of people with SUD in the Nordic prison population. Successful research in this field will have the potential to identify significant areas of benefit and will have important implications for ongoing policy related to interventions for SUD in the prison population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35182.
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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 | 0.060 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".