Purpose
This paper aims to examine the effectiveness of PhD support groups as an intervention that improves mental well-being and increases confidence in timely PhD completion.
Design/methodology/approach
Participants of six PhD support groups, which we co-facilitated, completed a survey at the start of the intervention and at the end of the eight weeks of attendance. The survey measured subjective well-being and confidence in completion using the Warwick-Edinburgh Mental Well-being Scale and statements from the Postgraduate Research Experience Survey (2017 and 2019). The final survey also included open-ended questions to identify the helpful factors of the intervention.
Findings
Participants’ subjective well-being scores increased considerably over the eight weeks of group attendance and improved from initial score ranges associated with risk of depression or psychological distress. As a result of feeling understood and supported by other group members, participants felt less isolated and anxious, were more satisfied with their life and work-life balance, and felt more confident about completing their PhD within the institutional time frame. The results confirm previous findings on the positive effects of social support and the relationship between poor well-being and attrition.
Practical implications
Support groups could form an integral part of university support as they increase well-being and could improve retention.
Originality/value
Existing literature mainly highlights factors that affect postgraduate researchers’ well-being, with limited research on innovative interventions. This paper investigates the impact of social support in a facilitated peer group that focuses on the emotional and psychological aspects of the PhD experience, rather than peer group learning or support with specific research tasks.
A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in a so-called “blend.” The current standard method of assessing blend likelihood in LSST images relies on counting up the number of intensity peaks in the smoothed image of a blend candidate, but the reliability of this procedure has not yet been comprehensively studied. Here we construct a realistic distribution of blended and unblended galaxies through high-fidelity simulations of LSST-like images, and from this we examine the blend classification accuracy of the standard peak-finding method. Furthermore, we develop a novel Gaussian process blend classifier model, and show that this classifier is competitive with both the peak finding method as well as with a convolutional neural network model. Finally, whereas the peak-finding method does not naturally assign probabilities to its classification estimates, the Gaussian process model does, and we show that the Gaussian process classification probabilities are generally reliable.
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