Assessing and Evaluating the Perfectionism Social Disconnection Model: Social Support, Loneliness, and Distress Among Undergraduate, Law, and Medical Students
Bibliographic record
Abstract
The current research evaluates the Perfectionism Social Disconnection Model (PSDM) by considering the links between measures of trait perfectionism and perfectionistic self-presentation and measures of social support, loneliness, and distress in cross-sectional research. A particular focus is on perfectionism and levels of social support as assessed by the Social Provisions Scale. The current study also uniquely evaluates levels of perfectionism and perfectionistic self-presentation in undergraduate students, medical students, and law students. The results across samples provided evidence that loneliness mediates the link between interpersonal perfectionism and distress in keeping with the predictions of the PSDM. Correlational results found robust links between loneliness and low levels of social support. Moreover, socially prescribed perfectionism and perfectionistic self-presentation were associated negatively with social support, and this was especially evident in terms of the facet tapping the nondisclosure of imperfections. Group comparisons of perfectionism yielded few significant differences in accordance with expectations. Levels of perfectionism tended to be lower among medical students. However, the links between perfectionism and distress were clearly evident among undergraduates, medical students, and law students, thus attesting to the vulnerability of perfectionistic students in general. Overall, the results further confirm the relevance of perfectionism in distress among students and applicability of the PSDM in various types of students.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| 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".