title: Problem-based learning using patient-simulated videos showing daily life for a comprehensive clinical approach.
abstract: OBJECTIVE
  We examined whether problem-based learning tutorials using patient-simulated videos showing daily life are more practical for clinical learning, compared with traditional paper-based problem-based learning, for the consideration rate of psychosocial issues and the recall rate for experienced learning.
  
  
  METHODS
  Twenty-two groups with 120 fifth-year students were each assigned paper-based problem-based learning and video-based problem-based learning using patient-simulated videos. We compared target achievement rates in questionnaires using the Wilcoxon signed-rank test and discussion contents diversity using the Mann-Whitney U test. A follow-up survey used a chi-square test to measure students' recall of cases in three categories: video, paper, and non-experienced.
  
  
  RESULTS
  Video-based problem-based learning displayed significantly higher achievement rates for imagining authentic patients (p=0.001), incorporating a comprehensive approach including psychosocial aspects (p<0.001), and satisfaction with sessions (p=0.001). No significant differences existed in the discussion contents diversity regarding the International Classification of Primary Care Second Edition codes and chapter types or in the rate of psychological codes. In a follow-up survey comparing video and paper groups to non-experienced groups, the rates were higher for video (χ2=24.319, p<0.001) and paper (χ2=11.134, p=0.001). Although the video rate tended to be higher than the paper rate, no significant difference was found between the two.
  
  
  CONCLUSIONS
  Patient-simulated videos showing daily life facilitate imagining true patients and support a comprehensive approach that fosters better memory. The clinical patient-simulated video method is more practical and clinical problem-based tutorials can be implemented if we create patient-simulated videos for each symptom as teaching materials.
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Akiko
authors.initials: A
authors.lastname: Ikegami
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Ohira
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Takanori
authors.initials: T
authors.lastname: Uehara
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Kazutaka
authors.initials: K
authors.lastname: Noda
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Shingo
authors.initials: S
authors.lastname: Suzuki
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Shikino
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Hideki
authors.initials: H
authors.lastname: Kajiwara
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kondo
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Hirota
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Masatomi
authors.initials: M
authors.lastname: Ikusaka
mesh_terms: D003430:Cross-Sectional Studies; D004501:Education, Medical / Q000379:methods*; D004521:Educational Measurement; D017144:Focus Groups; D006801:Humans; D016544:Patient Simulation; D018794:Problem-Based Learning / Q000379:methods*; D018709:Statistics, Nonparametric; D013337:Students, Medical; D014741:Video Recording
keywords: biopsychosocial; patient-simulated video; pbl; problem-based learning