The Influencing Factors of Subjective Well-being in Female Community Workers in Hangzhou
Bibliographic record
Abstract
Objective To understand the status of subjective well-being among female community workers and its influencing factors.Methods 167 female community workers were selected by stratified cluster sampling.Memorial University of Newfoundland Scale of Happiness(MUNSH),Social Support Rating Scale(SSRS),Trait Coping Style Questionnaire(TCSQ),Self-rating Anxiety Scale(SAS) and Self-rating Depression Scale(SDS) were used.Results Among the female community workers,the positive emotion and experience scores(4.43 ± 2.90 and 7.26 ± 3.94) of the subjective well-being were much higher than the negative emotion and experience scores(2.61 ± 2.52 and 4.37 ± 3.26) respectively.There was negative correlation between positive emotion and education level(r=-0.237,p0.01).Additionally,there were correlation between subjective well-being and anxiety,depression,coping style(positive response,negative response),social support(objective support,subjective support,degree of support utilization).The factors which influence life satisfaction were negative response,positive response,subjective support,degree of support utilization,SDS,education level(r=-4.757,p0.01;r=3.457,p0.01;r=2.985,p0.01;r=-2.722,p0.01;r=2.417,p0.05,respectively).Conclusion The general consciousness of female community workers in Hangzhou is happiness and satisfaction.The subjective well-being has close relation to education level,anxiety,coping style and social support..
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".