Bibliographic record
Abstract
Objective:To investigate the predictors of Subjective Well-being(SWB) in widowed elders.Methods:A total of 292 widowed elders who living in communities were selected by interval sampling.The subjects were interviewed with the Memorial University of Newfoundland Scale of Happiness,Cornell Medical Index(CMI),Coping Style Questionnaire,Social Support Rating Scale and a self-made questionnaire.The data was analyzed by independent-sample t-test,one-way ANOVA and Structural Equation Modeling.Results:The males reported higher Subjective Well-being than the females [(25.5±5.7)vs.(22.8±6.9),P0.01].CMI,problem-solving,withdrawal,social supports using,subjective social support were significantly correlated with SWB(the regression weight were-0.358,0.567,-0.469,0.320 and 0.152 respectively).Problem-solving,self-abuse,help-seeking,social supports using,subjective social support were significantly correlated with SWB(the regression weight were 0.124,-0.087,0.091,0.049 and 0.098 respectively).Structural Equation Modeling analysis showed that problem-solving had maximal positive effect on SWB,social supports using and subjective social secondly(the regression weight were 0.691,0.369 and 0.250 respectively);withdrawal had maximal negative effect on SWB(the regression weight was-0.469).Conclusion:Lack of social supports is a risk factor of low subjective well-being in widowed elders,and adaptive coping style have positive effect on SWB.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".