Objective: This study evaluated pharmacists' self-perceived competence and confidence to plan and conduct health-related research.
Method: This cross sectional study was conducted during the 89th Annual National Conference of the Pharmaceutical Society of Nigeria in November 2016. An adapted questionnaire was validated and administered to 200 pharmacist delegates during the conference.
Result: Overall, 127 questionnaires were included in the analysis. At least 80% of the pharmacists had previous health-related research experience. Pharmacist's competence and confidence scores were lowest for research skills such as: using software for statistical analysis, choosing and applying appropriate inferential statistical test and method, and outlining detailed statistical plan to be used in data analysis. Highest competence and confidence scores were observed for conception of research idea, literature search and critical appraisal of literature. Pharmacists with previous research experience had higher competence and confidence scores than those with no previous research experience (p<0.05). The only predictor of moderate-to-extreme self-competence and confidence was having at least one journal article publication during the last 5 years.
Conclusion: Nigerian pharmacists indicated interest to participate in health-related research. However, self-competence and confidence to plan and conduct research were low. This was particularly so for skills related to statistical analysis. Training programs and building of Pharmacy Practice Research Network are recommended to enhance pharmacist's research capacity.
MATERIALS AND METHODS: A total of 303 students (n = 303) responded to the online questionnaire. The first part of questionnaire was to evaluate the demographic data of the respondents and focused on the technique and management approach used for deep caries lesion. The second part investigated the preferred treatment used for deep caries based on the designated clinical case, while the third part assessed the factors that affected the decision on deep caries management.
STATISTICAL ANALYSIS: Independent t-test was used to compare difference between the two groups.
RESULTS: Seventy four percent of the students have the knowledge of the different methods of caries removal, while 25.8% were only familiar with complete caries removal. The preferred method for deep caries removal in permanent teeth was partial caries removal (53%). For primary dentition, 45.6% of the students prefer to perform pulpotomy as compared with other techniques. There was no significant difference in caries removal method for permanent teeth between undergraduate year of study (p > 0.05), which was partial caries removal at 52.7 and 53.5%, respectively. For primary dentition, the preferred caries removal method was pulpotomy for year 4 (39.8%) and year 5 (52%) students. The popular material to restore deep caries was resin composite (42%) followed by glass ionomer cement (23.3%).
CONCLUSIONS: This study showed that partial caries removal was the preferred method despite partial understanding on the identification of the clinical indicators of the technique.
OBJECTIVE: This article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.
METHODS: This study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.
RESULTS: The potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.
CONCLUSION: The reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.