Evidence map›Paper›PMID 37228710›Full record

ArticleProceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data2022

Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers.

Sajid Hussain Rafi Ahamed, Shahid Shakil, Hanjia Lyu, Xinping Zhang, Jiebo Luo

Open access · greenAbstract read
In one paragraph

Article in Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
15.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Sajid Hussain Rafi AhamedUniversity of Rochester.
Shahid ShakilUniversity of Rochester.
Hanjia LyuUniversity of Rochester.
Xinping ZhangUniversity of Rochester Medical Center.
Jiebo LuoUniversity of Rochester.
University of Rochester · USUniversity of Rochester Medical Center · US

Funding

Micro- and nanofiber enabled biomimetic periosteum for bone repair and reconstructionR01AR067859 · NIAMS · UNIVERSITY OF ROCHESTER · PI WANG, HONGJUN, ZHANG, XINPING · 2016 to 2020
$2.7M
Endothelial cell specification at the osteogenic and angiogenic interface in cranial bone tissue engineeringR01DE029790 · NIDCR · UNIVERSITY OF ROCHESTER · PI ZHANG, XINPING · 2020 to 2025
$2.7M
Molecular control of blood vessel types at the regenerative interface for engineering of osteogenic and angiogenic periosteum mimeticR01AR083224 · NIAMS · UNIVERSITY OF ROCHESTER · PI XINPING ZHANG · 2023 to 2026
$1.6M
Engineering structural bone allografts for enhanced repair and reconstructionR21AR076056 · NIAMS · UNIVERSITY OF ROCHESTER · PI XIE, JINGWEI, ZHANG, XINPING · 2020 to 2021
$365k
NIAMS NIH HHS R01 AR067859NIAMS NIH HHS R01 AR083224NIAMS NIH HHS R21 AR076056NIDCR NIH HHS R01 DE029790
6 · The paper itself

Abstract

Healthcare workers such as doctors and nurses are expected to be trustworthy and creditable sources of vaccine-related information. Their opinions toward the COVID-19 vaccines may influence the vaccine uptake among the general population. However, vaccine hesitancy is still an important issue even among the healthcare workers. Therefore, it is critical to understand their opinions to help reduce the level of vaccine hesitancy. There have been studies examining healthcare workers' viewpoints on COVID-19 vaccines using questionnaires. Reportedly, a considerably higher proportion of vaccine hesitancy is observed among nurses, compared to doctors. We intend to verify and study this phenomenon at a much larger scale and in fine grain using social media data, which has been effectively and efficiently leveraged by researchers to address real-world issues during the COVID-19 pandemic. More specifically, we use a keyword search to identify healthcare workers and further classify them into doctors and nurses from the profile descriptions of the corresponding Twitter users. Moreover, we apply a transformer-based language model to remove irrelevant tweets. Sentiment analysis and topic modeling are employed to analyze and compare the sentiment and thematic differences in the tweets posted by doctors and nurses. We find that doctors are overall more positive toward the COVID-19 vaccines. The focuses of doctors and nurses when they discuss vaccines in a negative way are in general

Identifiers

PMID37228710
PMCPMC10208360
OpenAlexW4318147475

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.