Back

COVID-19 vaccine hesitancy among Algerian medical students: a cross-sectional study in five universities.

Kerdoun, M. a.; Henni, H. A.; Yamoun, A.; Rahmani, A.; Kerdoun, R. M.; Elouar, N.

2021-08-31 public and global health
10.1101/2021.08.29.21261803 medRxiv
Show abstract

Vaccine hesitancy is a limiting factor in global efforts to contain the current pandemic, wreaking havoc on public health. As todays students are tomorrows doctors, it is critical to understand their attitudes toward the COVID-19 vaccine. To our knowledge, this study was the first national one to look into the attitudes of Algerian medical students toward the SARS-CoV-2 vaccine using an electronic convenience survey. 383 medical students from five Algerian universities were included, with a mean age of 21.02. 85.37% (n=327) of respondents had not taken the COVID-19 vaccine yet and were divided into three groups; the vaccine acceptance group (n=175, 53.51%), the vaccine-hesitant group (n=75, 22.93%), and the vaccine refusal group (n=77, 23.54%). Gender, age, education level, university, and previous experience with COVID-19 were not significant predictors for vaccine acceptance. The confirmed barriers to the COVID-19 vaccine concern available information, effectiveness, safety, and adverse effects. This work highlights the need for an educational strategy about the safety and effectiveness of the COVID-19 vaccine. Medical students should be educated about the benefits of vaccination for themselves and their families and friends. The Vaccine acceptant students influence should not be neglected with a possible ambassador role to hesitant and resistant students. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/21261803v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@fa156forg.highwire.dtl.DTLVardef@95676forg.highwire.dtl.DTLVardef@b9b712org.highwire.dtl.DTLVardef@a2095f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.