Associations Between Social Functioning and Indicators of University Student Engagement
Bibliographic record
Abstract
Less socially adaptive behaviors have often been underestimated in university students, with limited research addressing their impact on academic functioning. This study aimed to identify distinct profiles of social functioning difficulties in university students and to examine their associations with academic engagement, learning difficulties, and psychological distress. A cross-sectional, web-based survey was conducted with 540 undergraduate university students (mean age = 23.06, SD = 6.53; 89.7% female). Participants completed standardized self-report assessments of social functioning (SRS-2), academic engagement (SAES), learning difficulties (Vinegrad Plus), and psychological distress (GAD-7, PHQ-9). Latent profile analysis (LPA), based on ASD-related traits, revealed two latent profiles: one reflecting non-social functioning difficulties (311 participants, 57.6%—Profile 1) and another reflecting social functioning difficulties (229 participants, 42.4%—Profile 2), while binomial regression analyses examined their associations with academic outcomes. Participants in Profile 2 scored significantly higher than those in Profile 1 across all SRS-2 variables—awareness, cognition, communication, motivation, and restricted interests and repetitive behavior (p = 0.001)—indicating greater overall functioning in these domains. Students in the Non-social functioning difficulties profile showed higher levels of academic engagement in all areas. In contrast, students in the Social functioning difficulties profile experienced more self-reported learning challenges (p = 0.001), anxiety (p = 0.001), and depression (p = 0.001), underscoring the significant differences in social, academic, and emotional outcomes between the two profiles. These findings underscore the impact of vulnerability to social functioning difficulties on academic engagement, highlighting the need for tailored support systems within higher education settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".