The influence factors of subjective well-being in social workers of Hangzhou
Bibliographic record
Abstract
Objective To explore the present situation of the community workers' subjective well-being and the influence factors of them. Method 234 Hangzhou Urban community workers who were given by stratified by category,sub-paragraph, randomized, cluster sampling method were evaluated with the use of Memorial University of Newfoundland happy Scale (MUNSH), Social Support Scale (SSRS), characteristics of coping style questionnaire (TCSQ). Results On terms of Subjective well-being of community workers in Hangzhou,positive scores were higher than those of negative(11.79±6.88 and 7.74±5.90). The negative feelings and experiences of men were significant higher than those of women, ( t =3.807, P <0.01;t =2.610,P <0.01); positive feelings was negatively correlative with education level( r =-0.220,P <0.01); social support was significant positively correlative with positive response and subjective well-being( P <0.01).The influence factors of happiness were passive response,positive response,subjective support and availability(-5.766,P <0.01;4.050,P <0.01;2.616,P <0.05;2.286,P <0.05). Conclusion The community workers in Hangzhou felt well-being and satisfied in own. Subjective well-being had significant relationship with sex, education level, social support and coping style.Pay more attention to higher education ,male community workers ;reinforce social support,regulate coping style,there will be a positive effect to improve community workers' subjective well-being. Key words: Subjective well-being; Social worker; Social support; Coping Style
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".