Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Academic’s Perceptions of Barriers and Enablers to Oral Health Research
1
Zitationen
4
Autoren
2019
Jahr
Abstract
Background: Knowledge productivity and research engagement are essential to ascertain that the dental profession is at the frontier of a new discovery. Analysis of published papers on the Scopus database revealed that the dental field output from Malaysia is only a meagre 0.46% of the world's dental fields research output. This relatively low research output is a considerable hindrance to the prevention and management of oral ill health and its associated problems in Malaysia. This study aimed to identify the main motivational factors and barriers faced by Malaysian dental academics in conducting dental research. Method: An audio-recorded semi-structured face to face interview was conducted among academics in a public Malaysian institution to identify barriers and motivation factors for undertaking research activity and publication. Ten dental academics (Response rate= 83%) from various specialty backgrounds participated in this study. Qualitative data were analysed via thematic analysis, involving open-and close-coding, followed by identification of emerging themes. Results: The main motivating factors to undertake research activity and publishing reported by respondents were self-satisfaction, knowledge-improvement, information-sharing, encouragement from colleagues/institution, career progression and institution's requirement. The main barriers to undertaking research activities and publication were a time constraint, inadequate facilities, financial limitation, poor training in academic writing, as well as a lack of incentives or rewards. Factors affecting the choice of journal submission included Journal indexing (ISI/Scopus/WoS), reviewing time, journal impact factor, and publication fees. Conclusion: Findings of this study provide recommendations for stakeholders to overcome barriers to research and publication, leading to a more research-conducive environment in Malaysia.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.402 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.270 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.702 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.507 Zit.