Make or break: Succeeding in transition from incarceration
Bibliographic record
Abstract
Several factors impact successful reintegration after incarceration. We sought to better understand these factors such as pre-release preparedness or access to financial resources in provincial correctional facilities in Ontario, Canada with an underlying focus on the role of personal identification (PID) among people at risk of homelessness. We conducted a qualitative study with one-on-one telephone interviews. Eligibility criteria included having been released from a provincial correctional facility in the preceding 2 years, being over the age of 18, speaking English and having telephone access. Participants were recruited between February 2021 and July 2021. All interviews were audio recorded and transcribed. Data was analyzed using a thematic analysis framework along with strategies from grounded theory research. We interviewed 12 individuals and identified six key themes including 1) Degree of Preparedness Pre-Release 2) Managing Priorities Post-Release 3) Impact of Support Post-Release 4) Obstacles with Accessing Services 5) Influence of Personal Identification 6) Emotions and Uncertainty. We found that people with mental health and addiction challenges are uniquely at risk post-release. Solutions must include comprehensive and proactive case management that bridges the pre-release and post-release periods, simplified processes for obtaining PID, better connections to health and social services, and improved pre-release planning for community support.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".