@ See related articles pages 257 and 262
Bibliographic record
Abstract
In the province of Quebec, as elsewhere, HIV and hepati-tis C virus (HCV) infections in incarcerated populationsare of concern to public health and correctional service authorities.1–3 Many studies have shown that the prevalence of HIV infection is higher in the incarcerated population than in the general population,4–7 and more recently other studies have shown that a greater proportion of inmates are infected with HCV than with HIV.8–14 People admitted to correctional facilities often have a his-tory of injection drug use, needle-sharing and high-risk sex-ual behaviours.5,7,9,15 The practice of such risky behaviours is frequently continued during incarceration,5,9,10,15–17 along with other potentially risky activities, such as being tat-tooed.5,7,8,16 To date, there has been limited information re-garding the burden of HCV infection among inmates of provincial prisons in Quebec, where HIV and HCV testing is available to inmates only on request. Better knowledge of in-fection rates would help disease prevention and management program planning. We sought to determine the prevalence of HIV and HCV infections among inmates in 7 provincial prisons in Quebec and to identify risk factors associated with prevalent HCV in-fection in this population.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.890 | 0.597 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".