Predictive Model of Happiness on the Basis of Positive Psychology Constructs
Bibliographic record
Abstract
The purpose of the present study was to offer predictive model of happiness on the basis of positive psychology constructs. It is among correlational studies through applying causal modeling. The statistical population of the study included BA students at the University of Isfahan and Industrial University. A sample of 420 individuals was selected via multi-stage clustered random sampling method. The measurement instruments included Oxford’s happiness questionnaire (Argyle, 1989), psychological well-being (Ryff, 1989), quality of life (Cummins, 1997), Polotzin and Elison’s spiritual Well-Being (1982) and Bar-On’s Emotional Intelligence (2000). The data were analyzed through Amos22 software. Results showed that the model with supposed indexes owned an appropriate goodness of fit. The results implied that the greatest amount of variance of happiness is explained by the direct effect of quality of life and indirect effect of emotional intelligence with mediating of quality of life. The direct effect of emotional intelligence with mediating psychological wellbeing and indirect effect of emotional intelligence on happiness with mediating spiritual wellbeing explains the variable of happiness at medium level. Finally the role of positive psychology constructs especially quality of life and emotional intelligence is confirmed in happiness and the results state the importance of positive psychology constructs in happiness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".