Perceived Relational Evaluation as a Predictor of Self-Esteem and Mood in People with a Psychotic Disorder
Bibliographic record
Abstract
OBJECTIVE: There is evidence that social support predicts self-esteem and related moods for people with psychotic disorders. However, there has been little investigation of relative importance of specific components of social support. Evidence from social psychology suggests that perceived relational evaluation (PRE) or the extent to which people see others as valuing them, is a particularly important determinant of self-esteem and mood. Our study compared the importance of PRE and other types of social support, in predicting self-esteem and depressive mood, anxiety, and anger-hostility in a sample of patients in an early intervention program for psychotic disorders. METHOD: One hundred and two patients of the Prevention and Early Intervention Program for Psychoses in London, Ontario, completed measures of PRE, appraisal, tangible and general emotional social support, self-esteem, and mood. In addition, ratings of positive and negative symptoms were completed for all participants. RESULTS: In general, perceived relational value was the most important predictor of self-esteem and mood. These relations were not a result of confounding with positive or negative symptoms. CONCLUSIONS: PRE appears to be a particularly important aspect of social support in predicting self-esteem and mood states. Possible implications of these findings and future research directions are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".