Minding many minds: An assessment of mental health and resilience among undergraduate and graduate students; a mixed methods exploratory study

Keeping an eye out for change: Anxiety disrupts adaptive resolution of policy uncertainty

BACKGROUND

Human learning unfolds under uncertainty. Uncertainty is heterogeneous with different forms exerting distinct influences on learning. While one can be uncertain about what to do to maximize rewarding outcomes, known as policy uncertainty, one can also be uncertain about general world knowledge, known as epistemic uncertainty. In complex and naturalistic environments such as the social world, adaptive learning may hinge on striking a balance between attending to and resolving each type of uncertainty. Prior work illustrates that people with anxiety-those with increased threat and uncertainty sensitivity-learn less from aversive outcomes, particularly as outcomes become more uncertain. How does a learner adaptively trade-off between attending to these distinct sources of uncertainty to successfully learn about their social environment?

METHODS

We developed a novel eye-tracking method to capture highly granular estimates of policy and epistemic uncertainty based on gaze patterns and pupil diameter (a physiological estimate of arousal) RESULTS: These empirically derived uncertainty measures reveal that humans (N = 94) flexibly switch between resolving policy and epistemic uncertainty to adaptively learn about which individuals can be trusted and which should be avoided. However, those with increased anxiety (N = 49) do not flexibly switch between resolving policy and epistemic uncertainty, and instead express less uncertainty overall CONCLUSIONS: Combining modeling and eye-tracking techniques, we show that altered learning in people with anxiety emerges from an insensitivity to policy uncertainty and rigid choice policies, leading to maladaptive behaviors with untrustworthy people.

Exploring participants' experiences of mental health and stigma in American colleges

Objective: Mental illness is a growing public concern, particularly for persons in higher education. This research aims to distinguish mental health and stigma experiences between three academic communities: undergraduate students, graduate students, and faculty members. Methods: The researchers conducted semi-structured interviews with nineteen individuals who self-identified as a member of one or more of these groups. Findings and discussion: Participants reported experiencing a variety of mental health conditions (e.g., depression, anxiety, and post-traumatic stress), and described having diverse cognitive, affective, and behavioral responses to internally and externally derived stigma. The findings of this study also provide insights into the ways that academic communities observe and interpret on-campus communication about mental health, and participants offered impressions about ways to improve intervention and awareness messaging. Implications of the findings are discussed.

Detection and analysis of graduate students' academic emotions in the online academic forum based on text mining with a deep learning approach

Purpose

The possibility of mental illness caused by the academic emotions and academic pressure of graduate students has received widespread attention. Discovering hidden academic emotions by mining graduate students' speeches in social networks has strong practical significance for the mental state discovery of graduate students.

Design/methodology/approach

Through data collected from online academic forum, a text based BiGRU-Attention model was conducted to achieve academic emotion recognition and classification, and a keyword statistics and topic analysis was performed for topic discussion among graduate posts.

Findings

Female graduate students post more than male students, and graduates majoring in chemistry post the most. Using the BiGRU-Attention model to identify and classify academic emotions has a performance with precision, recall and F1 score of more than 95%, the category of PA (Positive Activating) has the best classification performance. Through the analysis of post topics and keywords, the academic emotions of graduates mainly come from academic pressure, interpersonal relationships and career related.

Originality

A BiGRU-Attention model based on deep learning method is proposed to combine classical academic emotion classification and categories to achieve a text academic emotion recognition method based on user generated content.

A mediating model of mindfulness, sense of purpose in life and mental health among Chinese graduate students

The purpose of the present study is to examine the relationship between mindfulness and mental health of graduate students and the mediating effects of sense of purpose in life on mindfulness and mental health. The participants include 419 graduate students from 6 universities in China, and there are 190 males and 229 females. The Hayes Process is adopted to analyze the effects of the sense of purpose in life on mindfulness and mental health of graduate students. The results reveal that mindfulness can effect the mental health of graduate students positively and significantly. The sense of purpose in life is found to mediate the relationship between mindfulness and mental health. In further moderated mediation analyses, the effect of mindfulness on mental health can be adjusted by family economic condition. The type of degree can adjust the effect of mindfulness on sense of purpose, and academic Interest can adjust the mediating effect of sense of purpose. Finally, this study discusses several empirical and methodological implications of the findings.