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65 results for “Vaccine hesitancy”
VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter
<p>We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: <em>pro-vaxx, anti-vaxx</em>, <em>vaxx-hesitant</em>,<em> or irrelevant</em>.</p> <p><strong>***</strong></p> <p><strong>Please use the V2 version.</strong></p> <p><strong>***</strong></p> <p>We split our dataset into two separate files:</p> <p>(1) VaccineHesitancy_train_v2.csv (Single + Double annotated)</p> <p>(2) VaccineHesitancy_test.csv (Double annotated)</p> <p>We present the details of this dataset here:</p> <p>VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023)</p> <p><strong>Our Pre-trained model</strong> (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert</p> <p><strong>Paper</strong>: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992</p> <p> </p> <pre>@inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } </pre> <p> </p> <p> </p> <p> </p>
Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India
<p>This dataset was created as part of our study on polio vaccine hesitancy in Malegaon, India. If using this dataset, please cite this data repository and the publication: Onnela JP, Landon BE, Kahn AL, Ahmed D, Verma H, O'Malley AJ, Bahl S, Sutter RW, Christakis NA. Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India. Soc Sci Med. 2016 Mar;153:99-106. doi: 10.1016/j.socscimed.2016.01.024. Epub 2016 Feb 4. PMID: 26889952.</p> <p> </p> <p><strong>A) Main data file data.csv</strong></p> <p>The files below can be constructed from this main file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 3: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 12: Primary ID for Alter 1 – general questions (Integer)<br> Col 13: Duplicate ID for Alter 1 – general questions (Integer)<br> Col 14: Primary ID for Alter 2 – general questions (Integer)<br> Col 15: Duplicate ID for Alter 2 – general questions (Integer)<br> Col 16: Primary ID for Alter 3 – general questions (Integer)<br> Col 17: Duplicate ID for Alter 3 – general questions (Integer)<br> Col 18: Primary ID for Alter 4 – general questions (Integer)<br> Col 19: Duplicate ID for Alter 4 – general questions (Integer)<br> Col 48: Primary ID for Alter 1 if not household head – general questions (Integer)<br> Col 49: Duplicate ID for Alter 1 if not household head – general questions (Integer)<br> Col 50: Primary ID for Alter 2 if not household head – general questions (Integer)<br> Col 51: Duplicate ID for Alter 2 if not household head – general questions (Integer)<br> Col 52: Primary ID for Alter 3 if not household head – general questions (Integer)<br> Col 53: Duplicate ID for Alter 3 if not household head – general questions (Integer)<br> Col 54: Primary ID for Alter 4 if not household head – general questions (Integer)<br> Col 55: Duplicate ID for Alter 4 if not household head – general questions (Integer)<br> Col 56: Primary ID for Alter 1 – health questions (Integer)<br> Col 57: Duplicate ID for Alter 1 – health questions (Integer)<br> Col 58: Primary ID for Alter 2 – health questions (Integer)<br> Col 59: Duplicate ID for Alter 2 – health questions (Integer)<br> Col 60: Primary ID for Alter 3 – health questions (Integer)<br> Col 61: Duplicate ID for Alter 3 – health questions (Integer)<br> Col 62: Primary ID for Alter 4 – health questions (Integer)<br> Col 63: Duplicate ID for Alter 4 – health questions (Integer)<br> Col 92: Primary ID for Alter 1 if not household head – health questions (Integer)<br> Col 93: Duplicate ID for Alter 1 if not household head – health questions (Integer)<br> Col 94: Primary ID for Alter 2 if not household head – health questions (Integer)<br> Col 95: Duplicate ID for Alter 2 if not household head – health questions (Integer)<br> Col 96: Primary ID for Alter 3 if not household head – health questions (Integer)<br> Col 97: Duplicate ID for Alter 3 if not household head – health questions (Integer)<br> Col 98: Primary ID for Alter 4 if not household head – health questions (Integer)<br> Col 99: Duplicate ID for Alter 4 if not household head – health questions (Integer)<br> Col 101: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 102: TV (1:Yes, 2:No)<br> Col 103: Phone (1:Yes, 2:No)<br> Col 104: Cooking cylinder (1:Yes, 2:No)<br> Col 105: Number of rooms (Integer)<br> Col 106: Toilet (1:Yes, 2:No)<br> Col 107: Number of people (Integer)</p> <p> </p> <p><strong>B) Derived network files (edge lists)</strong></p> <p>These directed network edge lists were derived from the main data file. Here N stands for number of nodes and L stands for number of edges.</p> <p>1) Nomination network (directed, general only): N=8161, L=7357; edgelist_dg.txt</p> <p>2) Nomination network (directed, health only): N=7223, L=5744; edgelist_dh.txt</p> <p>3) Nomination network (combined, directed): N=11828, L=11655; edgelist_d.txt</p> <p>4) Nomination network (combined, directed) LCC: N=6113, L=6647; edgelist_d_lcc.txt</p> <p>5) Vaccine network (directed): N=2428, L=1355; edgelist_g_vstatusok.txt</p> <p>6) Vaccine network (directed) LCC: N=710, L=813; edgelist_g_vstatusok_lcc.txt</p> <p> </p> <p><strong>C) Derived nodal attribute file final_node_data.csv</strong></p> <p>This nodal attribute file was derived from the main data file.</p> <p>Column: Variable (Value)<br> Col 0: Respondent ID (Integer)<br> Col 1: Data collection team ID (Integer)<br> Col 2: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles)<br> Col 3: In-degree – general questions (Integer)<br> Col 4: In-degree – health questions (Integer)<br> Col 5: In-degree – combined (Integer)<br> Col 6: Out-degree – general questions (Integer)<br> Col 7: Out-degree – health questions (Integer)<br> Col 8: Out-degree – combined (Integer)<br> Col 9: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education)<br> Col 10: TV (1:Yes, 2:No)<br> Col 11: Phone (1:Yes, 2:No)<br> Col 12: Cooking cylinder (1:Yes, 2:No)<br> Col 13: Number of rooms (Integer)<br> Col 14: Toilet (1:Yes, 2:No)<br> Col 15: Number of people (Integer)</p>
Repeated information of benefits reduces COVID-19 vaccination hesitancy: Experimental evidence from Germany
<p>This replication package contains the raw data and code to replicate the findings reported in the paper. The data and code are licensed under a Creative Commons Attribution 4.0 International Public License. See <strong>LICENSE.txt</strong> for details.</p> <p><strong>Software requirements</strong></p> <p>All analysis were done in Stata version 16:</p> <ul> <li>Add-on packages are included in <strong>scripts/libraries/stata</strong> and do not need to be installed by user. The names, installation sources, and installation dates of these packages are available in <strong>scripts/libraries/stata/stata.trk</strong>.</li> </ul> <p><strong>Instructions</strong></p> <ol> <li>Save the folder <strong>‘replication_PLOS’</strong> to your local drive.</li> <li>Open the master script <strong>‘run.do’</strong> and change the global pointing to the working direction (line 20) to the location where you save the folder on your local drive</li> <li>Run the master script <strong>‘run.do’</strong> to replicate the analysis and generate all tables and figures reported in the paper and supplementary online materials</li> </ol> <p><strong>Datasets</strong></p> <ul> <li>Wave 1 – Survey experiment: <strong>‘wave1_survey_experiment_raw.dta’</strong></li> <li>Wave 2 – Follow-up Survey: <strong>‘wave2_follow_up_raw.dta'</strong></li> <li>Map: shape-files <strong>‘plz2stellig.shp’ ‘OSM_PLZ.shp’</strong>, area codes <em><em>‘Postleitzahlengebiete</em>-_OSM.csv’</em>_, (all links to the sources can be found in the script ‘04_figure2_germany_map.do’)</li> <li>Pretest: <strong>‘pre-test_corona_raw.dta’</strong></li> <li>For Appendix S7: <strong>‘alter_geschlecht_zensus_det.xlsx’, ‘vaccination_landkreis_raw.dta’, ‘census2020_age_gender.csv’</strong> (all links to the sources can be found in the script ‘06_AppendixS7.do’)</li> <li>For Appendix S10: ‘<strong>vaccination_landkreis_raw.dta’</strong> (all links to the sources can be found in the script ‘07_AppendixS10.do’)</li> </ul> <p><strong>Descriptions of scripts</strong></p> <p><strong>1_1_clean_wave1.do</strong><br> This script processes the raw data from wave 1, the survey experiment<br> <strong>1_2_clean_wave2.do</strong><br> This script processes the raw data from wave 2, the follow-up survey<br> <strong>1_3_merge_generate.do</strong><br> This script creates the datasets used in the main analysis and for robustness checks by merging the cleaned data from wave 1 and 2, tests the exclusion criteria and creates additional variables<br> <strong>02_analysis.do</strong><br> This script estimates regression models in Stata, creates figures and tables, saving them to <strong>results/figures and results/tables</strong><br> <strong>03_robustness_checks_no_exclusion.do</strong><br> This script runs the main analysis using the dataset without applying the exclusion criteria. Results are saved in <strong>results/tables</strong><br> <strong>04_figure2_germany_map.do</strong><br> This script creates Figure 2 in the main manuscript using publicly available data on vaccination numbers in Germany.<br> <strong>05_figureS1_dogmatism_scale.do</strong><br> This script creates Figure S1 using data from a pretest to adjust the dogmatism scale.<br> <strong>06_AppendixS7.do</strong><br> This script creates the figures and tables provided in Appendix S7 on the representativity of our sample compared to the German average using publicly available data about the age distribution in Germany.<br> <strong>07_AppendixS10.do</strong><br> This script creates the figures and tables provided in Appendix S10 on the external validity of vaccination rates in our sample using publicly available data on vaccination numbers in Germany.</p>
Waterloo, Ontario Federal and Provincial Voting Intention and Vaccine Hesitancy
<p>This it the initial release of federal and provincial voting intention in Waterloo Region in the spring of 2022, commissioned by the Laurier Institute for the Study of Public Opinion and Policy.</p>
Attitude and CTM predictions for From Tribal Polarization to Socio-Economic Disparities: Exploring the Landscape of Vaccine Hesitancy on Twitter paper
<p>The presented data pertains to predictions of Attitudes and CTM, generated through the application of Machine Learning models. These models have been extensively elucidated in the research paper titled "From Tribal Polarization to Socio-Economic Disparities: Exploring the Landscape of Vaccine Hesitancy on Twitter". The aforementioned data is available to the public.</p>
Factors Driving Vaccine Hesitancy rrelated to vaccination of children with Covid-19 vaccine among Albanian parents
<p>A validated questionnaire composed of 33 elements was used for the purpose of this study</p> <p>The subjects of this study were parents of children aged 0-18 years old .</p> <p>Inclusion criteria were:albanian parents who understood and spoke well albanian, parents of children aged 0-18 years old, parents aged 18 years old and over. Exclusion criteria were: parents aged less than 18 years old, albanian parents who didn’t understand well albanian language, parents of children aged more than 18 years old.</p>
COVID-19 Vaccine Hesitancy Counseling Intervention for Pharmacists: A Stepped-Wedge Trial
ClinicalTrials.gov study NCT06547814. IPD Sharing: YES. Countries: 1. Publications: 5.
Addressing Vaccine Hesitancy and Increasing COVID-19 Vaccine Uptake Among African American Young Adults in the South
ClinicalTrials.gov study NCT05490329. IPD Sharing: YES. Countries: 1. Publications: 3.
COVID-19 Vaccine Hesitancy Counseling Intervention for Pharmacists
ClinicalTrials.gov study NCT05926544. IPD Sharing: YES. Countries: 1. Publications: 5.
COVID-19 Vaccine Hesitancy in the Pandemic's Third Year
<p>Dataset and code for June 2022 study, "<strong>COVID-19 Vaccine Hesitancy in the Pandemic's Third Year"</strong></p>
Investigation on the Hesitancy of COVID-19 Vaccination Among Liver Transplant Recipients in China
ClinicalTrials.gov study NCT05532592. IPD Sharing: NO. Countries: 1. Publications: 9.
Different forms of Scientific Literacy Predict Vaccine Hesitancy and COVID-19 Risk Perception
<p>This is a data set that accompanies a manuscript that is yet to be published. The data set includes demographic information for 461 participants, and their responses to four surveys: one on scientific methodology, one on scientific facts, one on vaccine hesitiancy, and one on risk estimates related to COVID-19. </p>
A review of HPV and HBV vaccine hesitancy, intention and uptake in the era of social media and COVID-19
Prior to the COVID-19 pandemic, the World Health Organization named vaccine hesitancy as one of the top 10 threats to global health. The impact of hesitancy on uptake of human papillomavirus (HPV) vaccines was of particular concern, given the markedly lower uptake compared to other adolescent vaccines in some countries, notably the United States. With the recent approval of COVID-19 vaccines coupled with the widespread use of social media, concerns regarding vaccine hesitancy have grown. However, the association between COVID-related vaccine hesitancy and cancer vaccines such as HPV is unclear. To examine the potential association, we performed two reviews using Ovid Medline and APA PsychInfo. Our aim was to answer two questions: (1) Is COVID-19 vaccine hesitancy, intention, or uptake associated with HPV or HBV vaccine hesitancy, intention, or uptake? and (2) Is exposure to COVID-19 vaccine misinformation on social media associated with HPV or HBV vaccine hesitancy, intention, or uptake? Our review identified few published empirical studies that addressed these questions. Our results highlight the urgent need for studies that can shift through the vast quantities of social media data to better understand the link between COVID-19 vaccine misinformation and disinformation and its impact on uptake of cancer vaccines.
Database of a Survey on COVID-19 Vaccination Hesitancy in Algerian Teaching Hospital
<p>This is the database relating to the survey that studied the factors associated with anti-covid19 vaccination among the staff of our hospital and university establishment.</p>
Effectiveness of an Intervention on Vaccine Hesitancy Among Pediatric Nurses and Pediatricians
ClinicalTrials.gov study NCT06489236. IPD Sharing: NO. Countries: 1. Publications: 19.
Dengue Vaccine Hesitancy Among International Travelers
ClinicalTrials.gov study NCT06418854. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Vaccine Hesitancy in Black/African Americans With Rheumatic Diseases
ClinicalTrials.gov study NCT05822219. IPD Sharing: YES. Countries: 1. Publications: 2.
Public Health Messages to Address Vaccine Hesitancy
ClinicalTrials.gov study NCT03395106. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Africa COVID-19 Vaccine Hesitancy
ClinicalTrials.gov study NCT04912284. IPD Sharing: Not stated. Countries: 5. Publications: 8.
The Effect of Newspaper Reporting on COVID-19 Vaccine Hesitancy: a Randomised Controlled Trial
ClinicalTrials.gov study NCT05582564. IPD Sharing: NO. Countries: 1. Publications: 4.
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