Old people's Happiness Degree and the Influence Factor Inquisitions in Parts of Yi 'nan County City Area
Bibliographic record
Abstract
[Objective]To explore the old people's happiness de gr ee and its affect factors.[Methods]249 old people of yinan cou nty city area were tested by using of the Memorial University of Newfoundland S cale of Happiness(MUNSH)and the multiple regression analysis was used to analys ed the happiness degree and its affect factors,in September to October,2004.[Results]249 old people's happiness degree gets a goal to 35.03±8.10,and there was no statistic difference between male and female.There were st atistic difference between different economic income degree and different hous ing degree.The positive temperament feeling experience different from generally positively sex experience get a goal,male get more goal than female,the higher the economic income,the higher the goal.housing area difference statisticses to learn the meaning.The spouse still living's general positive sex experience ge t a higher goal than those who lose spouse and divorce or single.General positi ve sex of husband and wife's inmate the experience get a higher goal than those who were lonely or living with old man or son and daughter.The negative temper ament feeling experience gets a goal a difference of each age to have to statist ics to learn the meaning.Diverse return to return analytical result enunciation gradually,to raise the happiness a related factor of old people is to cohabit wi th spouse,higher economic income,bigger housing area.[Conclusion]This old people's happiness degree regard positive temperament feeling as pri nciple,marital status,economic income and the housing area have important influe nce to old people's happiness degree.
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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 | 0.001 |
| 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".