Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study
Objectives The study aims to construct artificial neural networks that are capable of predicting willingness of previously infected academic dental hospital personnel (ADHP) to accept or recommend vaccines to family or patients. Methods: The study utilized data collected during a cross-sectional sur...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-12-01
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| Series: | Open Health |
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| Online Access: | https://doi.org/10.1515/openhe-2022-0028 |
| _version_ | 1797743806356914176 |
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| author | Abu-Hammad Osama Althagafi Nebras Abu-Hammad Shaden Eshky Rawah Abu-Hammad Abdalla Alhodhodi Aishah Abu-Hammad Malak Dar-Odeh Najla |
| author_facet | Abu-Hammad Osama Althagafi Nebras Abu-Hammad Shaden Eshky Rawah Abu-Hammad Abdalla Alhodhodi Aishah Abu-Hammad Malak Dar-Odeh Najla |
| author_sort | Abu-Hammad Osama |
| collection | DOAJ |
| description | Objectives The study aims to construct artificial neural networks that are capable of predicting willingness of previously infected academic dental hospital personnel (ADHP) to accept or recommend vaccines to family or patients. Methods: The study utilized data collected during a cross-sectional survey conducted among COVID-19 infected ADHP. A total of ten variables were used as input variables for the network and analysis was repeated 10 times to calculate variation in accuracy and validity of input variables. Three variables were determined by the best network to be the least important and consequently they were excluded and a new network was constructed using the remaining seven variables. Analysis was repeated 10 times to investigate variation of accuracy of predictions. Results: The best network showed a prediction accuracy that exceeded 90% during testing stage. This network was used to predict attitudes towards vacci-nation for a number of hypothetical subjects. The following factors were identified as predictors for undesirable vaccination attitudes: dental students who had an insufficient vaccine awareness, a long symptomatic period of illness, and who did not practice quarantine. Conclusions: It is concluded that vaccine awareness is the most important factor in predicting favorable vaccine attitudes. Vaccine awareness campaigns that target ADHP should give more attention to students than their faculty. |
| first_indexed | 2024-03-12T15:00:45Z |
| format | Article |
| id | doaj.art-b5a14539d08d41f8ad02f19ee0501245 |
| institution | Directory Open Access Journal |
| issn | 2544-9826 |
| language | English |
| last_indexed | 2024-03-12T15:00:45Z |
| publishDate | 2022-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Health |
| spelling | doaj.art-b5a14539d08d41f8ad02f19ee05012452023-08-14T07:07:53ZengDe GruyterOpen Health2544-98262022-12-013116817710.1515/openhe-2022-0028Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based StudyAbu-Hammad Osama0Althagafi Nebras1Abu-Hammad Shaden2Eshky Rawah3Abu-Hammad Abdalla4Alhodhodi Aishah5Abu-Hammad Malak6Dar-Odeh Najla7College of Dentistry, Taibah University, Al Madinah Al Munawara 43353, Saudi ArabiaCollege of Dentistry, Taibah University, Al Madinah Al Munawara 43353, Saudi ArabiaComprehensive Amman, Healthcare center, Amman, JordanCollege of Dentistry, Taibah University, Al Madinah Al Munawara 43353, Saudi ArabiaSchool of Medicine, University of Jordan, Amman11942, JordanCollege of Dentistry, Taibah University, Al Madinah Al Munawara 43353, Saudi ArabiaSchool of Medicine, Hashemite University, Zarqa, 13133, JordanCollege of Dentistry, Taibah University, Al Madinah Al Munawara 43353, Saudi Arabia; School of Dentistry, University of Jordan, Amman 11942, JordanObjectives The study aims to construct artificial neural networks that are capable of predicting willingness of previously infected academic dental hospital personnel (ADHP) to accept or recommend vaccines to family or patients. Methods: The study utilized data collected during a cross-sectional survey conducted among COVID-19 infected ADHP. A total of ten variables were used as input variables for the network and analysis was repeated 10 times to calculate variation in accuracy and validity of input variables. Three variables were determined by the best network to be the least important and consequently they were excluded and a new network was constructed using the remaining seven variables. Analysis was repeated 10 times to investigate variation of accuracy of predictions. Results: The best network showed a prediction accuracy that exceeded 90% during testing stage. This network was used to predict attitudes towards vacci-nation for a number of hypothetical subjects. The following factors were identified as predictors for undesirable vaccination attitudes: dental students who had an insufficient vaccine awareness, a long symptomatic period of illness, and who did not practice quarantine. Conclusions: It is concluded that vaccine awareness is the most important factor in predicting favorable vaccine attitudes. Vaccine awareness campaigns that target ADHP should give more attention to students than their faculty.https://doi.org/10.1515/openhe-2022-0028anncovid-19dental studentsdental facultypredictionvaccine acceptancevaccine awareness |
| spellingShingle | Abu-Hammad Osama Althagafi Nebras Abu-Hammad Shaden Eshky Rawah Abu-Hammad Abdalla Alhodhodi Aishah Abu-Hammad Malak Dar-Odeh Najla Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study Open Health ann covid-19 dental students dental faculty prediction vaccine acceptance vaccine awareness |
| title | Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study |
| title_full | Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study |
| title_fullStr | Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study |
| title_full_unstemmed | Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study |
| title_short | Factors Predicting Acceptance and Recommendation of Covid-19 Vaccines Among Previously Infected Academic Dental Hospital Personnel; An Artificial Intelligence-Based Study |
| title_sort | factors predicting acceptance and recommendation of covid 19 vaccines among previously infected academic dental hospital personnel an artificial intelligence based study |
| topic | ann covid-19 dental students dental faculty prediction vaccine acceptance vaccine awareness |
| url | https://doi.org/10.1515/openhe-2022-0028 |
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