Public health responses for the management of COVID-19 in detention centers. Literature review
Bibliographic record
Abstract
Objective To identify in the literature the recommendations for the prevention and control of COVID-19 in prisons and other preventive detention centers, in order to characterize the response lines. Materials and Methods 88 publications were identified in databases and digital repositories using key terms. After applying the PRISMA methodology, 18 publications were selected to carry out the qualitative analysis. The chosen publications refer to recommendations from academics, researchers and experts. 6 publications issued by the Governments of Canada, Belgium, France and United States of America were analyzed to make clear the government perspectives. Publications related to underage and psychiatric patients were not considered. ResultsAlthough there isn’t enough literature, it was possible to characterize the available recommendations, grouping them into 6 lines of action. Within these lines, the establishment of physical, administrative, legal, hygienic and health measures is considered essential. In addition, it is necessary to ensure the epidemiological management and adaptation of health services based on the burden of disease and susceptibility of the persons under arrest. Conclusions The response to COVID-19 in detention centers is complex and challenging. Therefore, the conventional steps like hygienic, sanitary, medical and epidemiological care aren’t enough. In fact, the adjustment of criminal and penitentiary policies and the transformation of the justice system are considered essential to reduce and control the residential density.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".