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Validity and Reliability of the Dental Neglect Scale among Romanian Adults.

Beatrice Adriana BalgiuRuxandra SfeatcuChristina MihaiRoxana Romanița IliciIoanina ParlatescuLaura Tribus
Published in: Journal of personalized medicine (2022)
The Dental Neglect Scale (DNS) is a well-known measure for assessing behaviours and attitudes related to oral health. However, the factor inconsistency revealed by the literature involves further investigations. The study focuses on the validation of the DNS in the case of a sample of the adult population from Romania. In this regard, data were collected online from 872 adults (616 females and 256 males). DNS reliability was examined from the perspective of internal consistency. Convergent validity was performed by associating DNS with different scales from the oral health field. In order to examine and confirm the factorial structure, the sample was broken down into two subsamples which made the subject of the exploratory factorial analysis (EFA) and confirmatory factorial analysis (CFA), respectively. DNS-RO is positively associated with the scale which measures the values related to oral health (OHVS) and negatively with those that assess the impact of the oral health on life quality (OHIP-14), the distrust of the benefits of oral health services (R-DBS), and reduced need for oral care (DIS). The Cronbach's α = 0.70, McDonald's ω = 0.70 and CR = 0.77 are acceptable. Both EFA and CFA (χ 2 /df = 1.13; CFI = 0.99; RMSEA = 0.017; SRMR = 0.059) support the unifactorial structure of the scale. The gender differences show that females evince greater care for oral health than male subjects. The study shows that the DNS-RO can be used to assess the behaviours and attitudes towards oral health in the case of the Romanian adult population in epidemiological studies and health promotion programs through health education.
Keyphrases
  • oral health
  • healthcare
  • health promotion
  • quality improvement
  • palliative care
  • public health
  • mental health
  • pain management
  • social media
  • deep learning
  • big data
  • artificial intelligence
  • deep brain stimulation