Person-Centered Associations between High- and Low-Risk Personality Profiles and Psychological Adjustment in University Students
Bibliographic record
Abstract
Personality traits are considered potential risk or protective factors for learning and psychological adjustment. This is a concern in higher education settings, which comprise mostly youth in emerging adulthood. The purpose of this study is to apply a person-centered approach to identify personality profiles of university students based on their character traits and then evaluate whether some clusters predict differences in emotional distress and coping strategies. We conducted a cross-sectional web-based survey with 467 southern Italian undergraduate university students ( M=21.8 , SD=3.69 ). Students completed an anonymous online survey and self-report questionnaires measuring sociodemographic characteristics, personality traits (Personality Inventory for DSM-5), emotional distress (General Anxiety Disorders-7, Patient Health Questionnaire-9), and coping strategies (Brief-COPE). Two distinct clusters were identified, differing in relation to maladaptive personality traits. One was characterized by high maladaptive personality traits, comprising 45.6% of the sample population. This high-risk profile evidenced higher levels of negative affect, detachment, psychoticism, antagonism, and disinhibition. A second cluster, with low maladaptive personality traits, represented the remainder of the sample. Participants featuring high maladaptive personality traits reported lower functioning in terms of avoidant coping strategies in comparison to the second low-risk cluster. Generating profiles of latent traits, such as in cluster analysis, can enhance a more profound theoretical understanding of underlying patterns within personality traits. This can enable higher education settings to meet variations in student needs by adapting their support services and interventions. Students can be trained to use coping strategies more effectively and efficiently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".