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Pacing Variation in Multistage Ultramarathons: Internet-Based Cross-Sectional Study
The GLM of pacing variation showed nonsignificant effects for gender (F1,36.2=2.5; P=.13; ηp2=0.063) and age group (F10,10=0.6; P=.80; ηp2=0.367), but it showed a significant interaction (gender × age) effect (F10,2686=2.3; P=.008; ηp2=0.009). Post hoc analyses showed that men have a higher pacing variation than women in U30, U35, U45, and U50. Additionally, the fastest women’s age group (U35) had the lowest pacing variation (Figure 3).
JMIR Form Res 2023;7:e46650
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Many countries mandate efforts to reduce unwarranted clinical variation in health care provided [10]. While some level of variation is required for innovation and learning, low levels of variation are generally thought to be best [11]. As stated in [12], “The idea is to hold on to variation across patients (to meet the needs of individual patients) and to limit variation across clinicians (which is driven by individual clinician preferences or differences in knowledge and experience)”.
JMIR Med Inform 2021;9(11):e30432
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We will quantify variation in peer-assessor assessments of a surgeon’s technical skills and use the intraclass correlation coefficient to measure interassessor reliability. We will use predictions of each surgeon’s technical skills from the linear mixed effect models as summary measures of a surgeon’s technical skills in subsequent analyses. Generalized linear mixed effect models with a logit link will then be used to associate a surgeon’s technical skills with our composite outcome.
JMIR Res Protoc 2021;10(1):e22536
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