Exploring why patients in heroin-assisted treatment are getting incarcerated—a qualitative study
Bibliographic record
Abstract
BACKGROUND: Heroin-assisted treatment has proven effective in reducing criminal offenses in opioid dependent individuals. Few studies attempted to explain the observed crime reduction and the reasons why these patients keep offending and getting incarcerated have to date not been explored. METHODS: Patients with a history of incarcerations during the time of participating in heroin-assisted treatment (n = 22) were invited to a semi-structured, narrative interview. Findings were evaluated with Mayring's qualitative content analysis framework. Additionally, the Montreal Cognitive Assessment test and the multiple-choice vocabulary intelligence test used to assess cognitive impairment and premorbid intelligence levels. RESULTS: Three main categories emerged in patients' narratives on their incarcerations: cocaine use, impaired functioning, and financial constraints. Lifetime prevalence of cocaine use disorder was 95.5% and their cocaine use often led to patients getting incarcerated. Impaired functioning mainly constituted the inability to receive and open mail. Financial constraints led to incarcerations in lieu of payment in 16 participants (72.7%). Categories overlapped notably and often occurred in close temporal proximity. A fourth category on the likelihood of getting incarcerated again in the future was inhomogeneous and ranged from the strong conviction to complete rejection of the scenario. Average premorbid intelligence levels were found, whereas the cognitive assessment suggested severe cognitive impairment in our sample. CONCLUSION: Participants mainly reported to have committed minor offenses and not being able to pay for resulting fines. The resulting prison sentences are an unconvincing practice from a medical and economic perspective alike. Public expenditure and the interruptions of the continuum of care could be reduced by legislatively protecting these marginalised patients.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".