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Partial Least Squares Structural Equation Path Modelling Determined Predictors of Students Reported Human Cadaver Dissection Activity.
Munabi, Ian G; Buwembo, William.
Afiliación
  • Munabi IG; Department of Anatomy, College of Health Sciences, Makerere University, Kampala, Uganda.
  • Buwembo W; Department of Anatomy, College of Health Sciences, Makerere University, Kampala, Uganda.
Forensic Med Anat Res ; 8(2): 18-37, 2020 Apr.
Article en En | MEDLINE | ID: mdl-32337321
ABSTRACT
Human cadaver dissection remains a core and preferred method of anatomical instruction at most low- and middle-income health professional training institutions. Dissection, which is both traumatic and stressful, sets the tone of the students' responses to later and or similar stressful learning opportunities like the post-mortems or care for terminally ill patients. Partial least squares structural equation modelling was used to determine the effect of the students' personality, perception of the learning environment, learning approach, and effect of the environment on the student, on undergraduate health professional student's activity in the human cadaver dissection room. This was a secondary analysis of previously collected data from a cross sectional survey of undergraduate health professional students. We found that personality type and perception of the environment had a positive effect on dissection room activity. Approach to learning and being affected by the dissection room experience (impact), had a negative effect on dissection room activity. All the above effects on dissection room activity were not significant. This study showed that personality, perception of the learning environment, learning approach and effect of the environment on the student, had effects on undergraduate health professional student's activity in the human cadaver dissection room. The modelled effects are opportunities for educational interventions aimed at increasing student activity in the dissection room.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_recursos_humanos_saude Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Forensic Med Anat Res Año: 2020 Tipo del documento: Article País de afiliación: Uganda

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_recursos_humanos_saude Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Forensic Med Anat Res Año: 2020 Tipo del documento: Article País de afiliación: Uganda
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