In the minds of OSCE examiners: uncovering hidden assumptions.

The Objective Structured Clinical Exam (OSCE) is a widely used method of assessment in medical education. Rater cognition has become an important area of inquiry in the medical education assessment literature generally, and in the OSCE literature specifically, because of concerns about potential compromises of validity. In this study, a novel approach to mixed methods that combined Ordinal Logistic Hierarchical Linear Modeling and cognitive interviews was used to gain insights about what examiners were thinking during an OSCE. This study is based on data from the 2010 to 2014 administrations of the Clinician Assessment for Practice Program OSCE for International Medical Graduates (IMGs) in Nova Scotia. An IMG is a physician trained outside of Canada who was a licensed practitioner in a different country. The quantitative data were examined alongside four follow-up cognitive interviews of examiners conducted after the 2014 administration. The quantitative results show that competencies of (1) Investigation and Management and (2) Counseling were highly predictive of the Overall Global score. These competencies were also described in the cognitive interviews as the most salient parts of OSCE. Examiners also found Communication Skills and Professional Behavior to be relevant but the quantitative results revealed these to be less predictive of the Overall Global score. The interviews also reveal that there is a tacit sequence by which IMGs are expected to proceed in an OSCE, starting with more basic competencies such as History Taking and building up to Investigation Management and Counseling. The combined results confirm that a hidden pattern exists with respect to how examiners rate candidates. This study has potential implications for research into rater cognition, and the design and scoring of practice-ready OSCEs.

Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest.

Most North American forests are at some stage of post-disturbance regrowth, subject to a changing climate, and exhibit growth and mortality patterns that may not be closely coupled to annual environmental conditions. Distinguishing the possibly interacting effects of these processes is necessary to put short-term studies in a longer term context, and particularly important for the carbon-dense, fire-prone boreal forest. The goals of this study were to combine dendrochronological sampling, inventory records, and machine-learning algorithms to understand how tree growth and death have changed at one highly studied site (Northern Old Black Spruce, NOBS) in the central Canadian boreal forest. Over the 1999-2012 inventory period, mean tree diameter increased even as stand density and basal area declined significantly. Tree mortality averaged 1.4 ± 0.6% yr-(1), with most mortality occurring in medium-sized trees; new recruitment was minimal. There have been at least two, and probably three, significant influxes of new trees since stand initiation, but none in recent decades. A combined tree ring chronology constructed from sampling in 2001, 2004, and 2012 showed several periods of extreme growth depression, with increased mortality lagging depressed growth by ~5 years. Higher minimum and maximum air temperatures exerted a negative influence on tree growth, while precipitation and climate moisture index had a positive effect; both current- and previous-year data exerted significant effects. Models based on these variables explained 23-44% of the ring-width variability. We suggest that past climate extremes led to significant mortality still visible in the current forest structure, with decadal dynamics superimposed on slower patterns of fire and succession. These results have significant implications for our understanding of previous work at NOBS, the carbon sequestration capability of old-growth stands in a disturbance-prone landscape, and the sustainable management of regional forests in a changing climate.