Cognitive and Affective Well-Being Differences in Subjective and Objective Socioeconomic Status Groups
Bibliographic record
Abstract
Objective: This study aimed at identifying the relationship between socioeconomic status and psychological well being in the Lithuanian population. Background: Socioeconomic status implies that not all individuals have equal opportunities to achieve their goals, because not everyone has equal access to education, health, even business support services, and the psychological well-being of some may be significantly reduced solely by lack of material resources. Method: The main method of research in the article is an interview method at the respondent's home. Also, various assessment tools were used in the Lithuanian population survey. In this survey, the authors applied the following scales: Flourishing Scale; The Satisfaction with Life Scale; The Cantril Self-Anchoring Striving Scale and The Positive and Negative Emotional Experience Scale. Results: The results of the study showed statistically significant differences in psychological well-being (psychological flourishing, life satisfaction, happiness, positive and negative emotional experiences) between different income quintile groups, with average psychological well-being constructs in the lowest income quintile being about twice lower than in the highest income quintile. The study showed that the mean ranks of the happiness score in the most deprived group were almost seven times lower than in the middle class. Conclusion: The research has established that strong and lasting negative emotional experiences are related to diminished well-being and can cause direct and indirect public damages. Authors indicate that additional research is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".