Exploring Online Health Reviews to Monitor COVID-19 Public Health Responses in Alabama State Department of Corrections: Case Example
Bibliographic record
Abstract
BACKGROUND: COVID-19, caused by SARS-CoV-2, has devastated incarcerated people throughout the United States. OBJECTIVE: The purpose of this study was to test the feasibility and acceptability of a COVID-19 Health Review for Correctional Facilities. METHODS: The COVID-19 Health Review survey for the Department of Corrections was developed in Qualtrics to assess the following: (1) COVID-19 testing, (2) providing personal protective equipment, (3) vaccination procedures, (4) quarantine procedures, (5) COVID-19 mortality rates for inmates, (6) COVID-19 mortality rates for correctional officers and prison staff, (7) COVID-19 infection rates for inmates, (8) COVID-19 infection rates for correctional officers and prison staff, and (9) uptake of COVID-19 vaccines. The estimated time to review the Alabama State Department of Corrections COVID-19 responses on their website and complete the survey items was 45 minutes to 1 hour. RESULTS: Of the 21 participants who completed the COVID-19 Health Review for Correctional Facilities survey, 48% (n=10) identified as female, 43% (n=9) identified as male, and 10% (n=2) identified as transgender. For race, 29% (n=6) self-identified as Black or African American, 24% (n=5) Asian, 24% (n=5) White, 5% (n=1) Pacific Islander or Native Hawaiian, and 19% (n=4) Other. In addition, 5 respondents self-identified as returning citizens. For COVID-19 review questions, the majority concluded that information on personal protective equipment was "poor" and "very poor," information on COVID-19 testing was "fair" and above, information on COVID-19 death/infection rates between inmates and staff was "good" and "very good," and information on vaccinations was "good" and "very good." There was a significant difference observed (P=.03) between nonreturning citizens and returning citizens regarding the health grade review with respect to available information on COVID-19 infection rates. CONCLUSIONS: COVID-19 health reviews may provide an opportunity for the public to review the COVID-19 responses in correctional settings.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".