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Predicting the effectiveness of the online clinical clerkship curriculum: Development of a multivariate prediction model and validation study.

ABSTRACT: Given scientific and technological advancements, expectations of online medical education are increasing. However, there is no way to predict the effectiveness of online clinical clerkship curricula. To develop a prediction model, we conducted cross-sectional national surveys in Japan. Social media surveys were conducted among medical students in Japan during the periods May-June 2020 and February-March 2021. We used the former for the derivation dataset and the latter for the validation dataset. We asked students questions in three areas: 1) opportunities to learn from each educational approach (lectures, medical quizzes, assignments, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, and interactive discussions with physicians) in online clinical clerkships compared to face-to-face, 2) frequency of technical problems on online platforms, and 3) satisfaction and motivation as outcome measurements. We developed a scoring system based on a multivariate prediction model for satisfaction and motivation in a cross-sectional study of 1,671 medical students during the period May-June 2020. We externally validated this scoring with a cross-sectional study of 106 medical students during February-March 2021 and assessed its predictive performance. The final prediction models in the derivation dataset included eight variables (frequency of lectures, medical quizzes, oral presentations, observation of physicians' practice, clinical skills practice, participation in interprofessional meetings, interactive discussions with physicians, and technical problems). We applied the prediction models created using the derivation dataset to a validation dataset. The prediction performance values, based on the area under the receiver operating characteristic curve, were 0.69 for satisfaction (sensitivity, 0.50; specificity, 0.89) and 0.75 for motivation (sensitivity, 0.71; specificity, 0.85). We developed a prediction model for the effectiveness of the online clinical clerkship curriculum, based on students' satisfaction and motivation. Our model will accurately predict and improve the online clinical clerkship curriculum effectiveness.

SUBMITTER: Kuroda N

PROVIDER: S-EPMC8794117 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Longitudinal Integrated Clerkships (LICs) are innovative educational models that allow medical student continuity with patients, preceptors, colleagues, and health care systems. Given their benefits, the number of LICs continues to increase. We share a pilot model for an ophthalmology LIC curriculum at the University of Colorado School of Medicine targeted for students to see patients through transitions of care. Methods A needs assessment was performed including literature search, interviews with expert faculty, and a precurricular student questionnaire. Based on our findings, we developed a pilot two-part curriculum consisting of an introductory lecture and a half-day clinical experience designed to integrate patient eye care into the LIC model. At the end of the year, students completed a questionnaire assessing attitude, confidence, and knowledge. Precourse data were collected from students in the academic year (AY) 2018/2019 to aid with the needs assessment. Postcourse data were collected after completion of the curriculum from students in AY 2019/2020. Data from questionnaire were intended to improve our curricular experience. Results Our curriculum was piloted between the 2019 and 2020 AY. The completion rate of our curriculum was 100%. The questionnaire response rate was 90% in pre- and postcurricular groups ( n =15/17 and n =9/10, respectively). Hundred percent of students from both groups responded that it is "very important"/"important" for all physicians to be able to identify when ophthalmology referral is indicated. After the intervention, there were significant differences in the rate of students responding that they were "confident" diagnosing acute angle-closure glaucoma (36 vs. 78%, p =0.04), treating a chemical burn (20 vs 67%, p =0.02), and diagnosing viral conjunctivitis (27 vs. 67%); 90% of students reported increased confidence in longitudinal care of patients in the eye clinic. Conclusions Medical students believe in the importance of ophthalmic education regardless of their specialty of choice. We present a pilot model to introduce ophthalmology within an LIC model. Future studies with a larger sample are needed to determine the impact of this model in terms of knowledge acquisition and relationship between curriculum and ophthalmology interest among students. Our curriculum can be adapted to other underrepresented specialties in the medical school curriculum and is easily exportable to other LICs.

Project description:Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ?2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ?2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

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Predicting the effectiveness of the online clinical clerkship curriculum: Development of a multivariate prediction model and validation study.

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