The Satisfaction with Life Scale and the Subjective Well-Being Inventory in the General Korean Population: Psychometric Properties and Normative Data
Bibliographic record
Abstract
This study aims to evaluate the psychometric properties of the Satisfaction with Life Scale (SWLS) and the Subjective Well-Being Inventory (SWBI) in a nationally representative sample in Korea. A total of 1200 people completed the semi-structured, self-reported questionnaire, which included five items from the SWLS and 14 items from the SWBI. All items and the total score of both the SWLS and the SWBI showed high internal consistency (with Cronbach’s alphas of 0.886 and 0.946, respectively). The item-total correlation values for both measures were in the ranges of 0.71–0.75 and 0.65–0.80, respectively. There were positive correlations between the SWLS and SWBI (r = 0.59, p = 0.01). The SWLS, SWBI and global well-being (GWB) scores were positively correlated with the McGill Quality of Life subscales (p = 0.01) but negatively correlated with the Patient Health Questionnaire-9 (p = 0.01). Participants under 50 years old (adjusted odds ratio [aOR] = 1.30, 95% confidence interval [CI] = 1.00–1.69) and those in rural areas (aOR = 1.63, 95% CI = 1.28–2.07) had higher scores on the SWLS than other participant groups. Participants who were under 50 years old (aOR = 1.47, 95% CI = 1.12–1.92), were male (aOR = 1.33, 95% CI = 1.04–1.71), were married (aOR = 1.51, 95% CI = 1.13–2.01), lived in rural areas (aOR = 2.30, 95% CI = 1.35–3.91), or had higher incomes (aOR = 1.30, 95% CI = 1.02–1.65) showed higher SWBI scores. This study showed that the SLWS and SWBI have good psychometric properties and could be applicable to Korea.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".