“To come out of jail, it’s like, you’re lost” : psychological barriers women experience during community reintegration after incarceration
Bibliographic record
Abstract
As the fastest-growing prison population worldwide, women face distinct barriers to community reintegration after incarceration.Gender disparities substantially impact mental and emotional wellness, overlapping with negative coping strategies (e.g.substance use) and, therefore, involvement with the legal system.The current thesis includes two studies that examined the obstacles and distinct needs that women experience after incarceration.Using survey responses, study one explored the relationship between psychological barriers (i.e.selfesteem, self-stigma, social support, loneliness, and trauma) and prosocial reintegration and community connection.Results showed that self-esteem, social support, self-stigma, and loneliness are associated with reintegration and community connection.Through conducting interviews, study two investigated women's experiences and the barriers to reintegration.Results showed four overarching themes: connection as a pillar of healing, individual barriers, structural and systemic barriers, and ways forward.These findings demonstrate the need for additional resources and an improved release plan recognizing the obstacles women face during reintegration.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".