Associations between predictor variables and mental health difficulties among autistic and non-autistic UK undergraduate students
Why this work is in the frame
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Bibliographic record
Abstract
The transition to university occurs during a critical developmental period in which young people are expected to separate from their homes and assume greater responsibility for their daily lives. During this time, there is rapid cognitive development, changing interpersonal and sexual relationships, burgeoning professional responsibilities, and increasing susceptibility to stress (Chung & Hudziak, 2017). Duffy et al., (2019) indicated that the transition to university is a high-risk period of life for the onset of mental health problems, but it also provides an important chance for effective prevention. However, based on a report from higher education institutions in the UK, there is a significant gap between the growing demand for university student mental health support and the services available (Thorley, 2017). Duffy et al., (2020) identified distal and proximal risk factors for mental health problems and academic outcomes in first-year university students. According to their conceptual model, distal risk factors include a family history of mental health problems and early adversity (neglect, trauma, and abuse). Proximal risk factors consist of sleep problems, substance use, lack of social support and exercise, low self-esteem, and a high amount of perceived stress. These risk factors have been associated with anxiety, depression, and suicidal ideation among first-year students, while early adversity was associated with lower cumulative grades. Little is known about the experiences and outcomes for autistic undergraduates. There is clear evidence that autistic individuals experience more co-occurring mental health problems than the general population (Ameis & Szatmari, 2015; Lai et al., 2019). Considering the challenges faced by university students generally, autistic undergraduates may present with additional risk factors (e.g., COVID-19) that are associated with poorer mental health outcomes. Few studies have been conducted among autistic university students to explore mental health problems, and only one has taken place in the UK (Gurbuz et al., 2019), with another in the USA (Jackson et al., 2018), and another in Canada (McMorris et al., 2019). Their focus was received support, social and academic experiences (Gurbuz et al., 2019), mental health, academic and social experiences (Jackson et al., 2018), and mental health problems and utilisation of service use (McMorris et al., 2019). While having included small samples of autistic students, the authors pointed to increased mental health problems among autistic university students compared to those without autism. However, these studies did not explore how various factors may relate to mental health problems among autistic students. As a consequence, there is a significant knowledge gap about the mental health trajectories of autistic and non-autistic undergraduates, how they may differ, and what factors may explain any difference. Further, to date, only a single study has been conducted that examined gender differences in mental health problems among autistic individuals, not specifically university students, (Sedgwick et al., 2021) which warrants further exploration. Therefore, the current research study will explore the relationships between a series of hypothesised predictors and mental health problems (anxiety, depression, and suicidality) among undergraduate autistic and non-autistic students using a cross-sectional and longitudinal design. Gender differences will also be examined between autistic and non-autistic undergraduates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it