Collateral consequences of COVID-19 for people on probation and parole
Bibliographic record
Abstract
BACKGROUND: While the severe detrimental impact of COVID-19 on incarcerated people is well known, little is known about the experience of COVID-19 on those on community supervision. Our objective was to better understand the experience of the COVID-19 pandemic and its collateral consequences for those on community supervision (e.g., probation, parole). Beginning in December 2020, we conducted 185 phone surveys about COVID-19 with participants in The Southern Pre-Exposure Prophylaxis (PrEP) Study across its three sites - Florida, Kentucky, and North Carolina. We conducted rapid assessment interviews with both closed- and open-ended questions. We calculated descriptive statistics for close-ended questions and conducted a content analysis for open-ended questions. RESULTS: The COVID-19 pandemic affected those on community supervision through their experiences in the community and while incarcerated with over one-quarter of participants being reincarcerated during this time. In addition to many (128/185) experiencing COVID-19 symptoms, about half (85/185) of participants reported a diagnosis in their network with 16 of those participants losing loved ones to the pandemic. Participants experienced disruptions to their social network, healthcare, and livelihoods. Though many maintained their support systems, others felt isolated and depressed. Experiences during COVID-19 exacerbated difficulties already faced by those with criminal involvement. CONCLUSIONS: The public health community must recognize those experiencing probation and parole, not only those housed in carceral facilities, as disproportionately impacted by the COVID-19 pandemic. We must tailor programs and services to meet their needs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".