The Validity of the Smart Management Strategy for Health Assessment Tool-Life (SAT-Life) in General Population
Bibliographic record
Abstract
Abstract This study aimed to determine the reliability and validity of the life version of the Smart Management Strategy for Health Assessment Tool (SAT-Life) for the general South Korean population. We recruited 1,200 respondents using an equal-probability sampling method from March to May 2018. A life version of the Smart Management Strategy for Health Assessment Tool (SAT-Life) Cronbach’ α was used to determine the reliability of the 30-item SAT-Life. Comparisons with the Self-Reported Health Status (SRHS), Short Form-12 (SF-12), McGill Quality of Life Questionnaire (MQOL), Patient Health Questionnaire-9 (PHQ-9), and Satisfaction with Life Scale (SWLS) were conducted for establishing the discriminant and concurrent validity. Three sets of SAT-Life demonstrated a high reliability with good internal consistency (Cronbach’ α 0.86 for SAT-Core, 0.88 for SAT-Preparation, and 0.86 for SAT-Implementation). The total scores of the three SAT differentiated well between the before-maintenance group versus the maintenance group for achieving goals in the participants, and between the group with high depression score versus that with lower depression score in the general population. These SAT-Life scores were positively associated with health status and physical and mental quality of life (QOL). The scores of the SAT-Life were correlated with social support and spiritual well-being measured using the MQOL, and life satisfaction measured using SWLS. The 30-item SAT-Life had a satisfactory internal consistency and validity for the general South Korean population. Further validation should be carried out in a cohort study across different cultures, including an assessment of sensitivity.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".