Personality development and adjustment in college: A multifaceted, cross-national view.
Bibliographic record
Abstract
The current study is among the first to examine rank-order stability and mean-level change across college in both broad Big Five personality trait domains (e.g., Neuroticism) and the narrower facets underlying these domains (i.e., self-reproach, anxiety, and depression). In addition, the current study tests longitudinal associations between Big Five domains and facets and 3 aspects of adjustment: self-esteem, academic adjustment, and social adjustment in college. Specifically, the study examines codevelopment (correlated change), personality effects on later changes in adjustment, and adjustment effects on later changes in personality. Two large longitudinal samples from different countries were employed. Results suggested that rank-order stabilities of facets were generally large (i.e., >.50) across samples, and comparable with those observed for trait domains. Mean-level findings were largely in line with the maturity principle: levels of neuroticism and (most of) its facets decreased, whereas levels of the other domains and facets were either stable or increased. However, patterns sometimes slightly differed between facets of the same trait domain. All 3 types of longitudinal associations between personality and adjustment were found, but unlike mean-level change often varied by facet. The Extraversion facet of positive affect and the Conscientiousness facets of goal-striving and dependability were positively associated with all 3 adjustment indicators in both samples, whereas the Neuroticism facets of depression and self-reproach were consistently negatively associated with adjustment. In sum, our findings demonstrate that considering Big Five trait facets may be useful to reveal the nuanced ways in which personality develops in tandem with adjustment in college. (PsycINFO Database Record
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".