Beyond Clinical Care: How Gender Shapes Patient Perceptions of Dental Services in Nepal
DOI:
https://doi.org/10.3126/njmr.v8i4.85640Keywords:
Dental care perceptions, gender differences, patient satisfaction, Nepal, bootstrap analysis, healthcare qualityAbstract
Background: Perceptions of dental care quality by patients are guided by several factors, such as gender, but relatively few studies have addressed these differences in Nepalese contexts. It is important to understand how male and female patients assess physical infrastructure, professionalism of staff, and interaction processes in order to offer gender-sensitive dental services.
Objective: The objective of the current study was to assess gender differences regarding dental treatment perception among patients at Kantipur Dental Hospital, Kathmandu, and physical structure, staff behavior, professionalism, and interaction quality.
Methods: The cross-sectional study involved 196 dental patients who were conveniently sampled. The data were gathered using structured questionnaires and were analyzed using independent samples *t*-tests, with Levene's test of variance homogeneity and bootstrap validation (1000 samples) for sensitivity. Statistical significance was at a level of *p* < 0.05.
Findings: Significant gender differences were found in perceptions of staff structure (*p* = 0.008), professionalism (*p* < 0.001), and processes of interaction (*p* < 0.001) with female patients scoring these dimensions significantly higher than males. No difference was found on perceptions of physical structure (*p* = 0.131). Bootstrap analysis confirmed the stability of these findings.
Conclusion: Dental care perceptions of patients are highly shaped by gender, particularly in interpersonal and professionalism domains. These findings emphasize the need for gender-sensitive communication training as well as dental clinic service design.
Novelty: To the best of our knowledge, this study is among the first to quantify gender differences in the perception of dental care in Nepal through rigorous statistical validation (bootstrap) in ascertaining result reliability.
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