Veterans aging in place behind bars: A structured living program that works.
Bibliographic record
Abstract
Although the absolute proportion of veterans compared with nonveterans in state and federal prison continues to decline, the number of older adult veterans who are imprisoned is rising. A multifaceted structured living program for geriatric prisoners in Nevada provides for the psychological, physical, and spiritual needs of aging incarcerated veterans. We hypothesized that the older adult veterans group would show more evidence of psychological and physical dysfunction, poor adjustment, and more life dissatisfaction than nonveterans because of high rates of posttraumatic stress disorder and depression among this subgroup. Our second hypothesis was that both the veteran and nonveteran older adult prisoner groups would report that they benefited from the True Grit treatment program. Finally, because substantial components of True Grit program were designed to treat combat-related issues, we hypothesized that the older adult veteran group would be more satisfied with the True Grit program than the nonveteran group. Evaluation results from 111 inmates indicated high life satisfaction and daily physical functioning, low psychological distress (depression, anxiety, and somatization), moderate prison context stress, and extremely high satisfaction with this program. The True Grit program provides a supportive environment fostering more adaptive coping, thereby ameliorating a certain amount of prison stress, while promoting a healthier aging in place experience for the elder inmates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".