Explorative Study Regarding Influenza Vaccine Hesitancy Among HIV-infected Patients
Bibliographic record
Abstract
There are scarce data regarding flu vaccination among people with HIV infection (PWHIV). The goal of this explorative study is to assess hesitancy toward influenza vaccination in a group of PWHIV during the pandemic. A questionnaire was administered to 219 patients vaccinated at our clinic during the 2020-2021 campaign. It evaluated subjects’ adherence over the last 3 seasonal vaccination campaigns, vaccine confidence, complacency and convenience, and the effect of the pandemic on the choice to vaccinate. The population was divided into two groups: fully adherent (all 3 campaigns, 117 patients) and non-fully adherent (1 or 2 campaigns, 102 patients). Adherence increased in non-fully adherent group in 2020-2021, but the pandemic did not affect the choice. Misbelieves emerged: influenza vaccine was considered protective SARS-CoV-2 (22.8% of total population); almost half of all patients thought influenza vaccine could improve their CD4+ cell level (57.3% in fully adherent, 40.2% in non-fully adherent, p<0.05). A quarter of the non-fully adherent group would not have vaccinated in a location other than our clinic (24.5% vs 11.9% in fully adherent group, p<0.05). Conclusively, offering a secure and private space for vaccination seems to encourage vaccination; healthcare professionals should improve counselling to increase adherence and correct misbeliefs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".