Correlation between mental health and subjective well-being in aged people of rural areas of Chongqing
Bibliographic record
Abstract
Objective To investigate the relationship between the metal health and subjective well-being of aged people in rural area of Chongqing.Methods Multistage stratified random sampling method was performed,and a total of 588 aged people more than 60 years old in rural area of Chongqing were investigated.Psychological health status and subjective well-being were evaluated by using Self-Reporting Inventory(SCL-90)and Memorial University of Newfoundland Scale of Happiness(MUNSH)respectively.Results The scores of SCL-90 ranged from 91 to 199and mean score was(109.13±11.81),which were significant lower than Chinese adult norm(P0.05).The scores of MUNSH ranged from 6to 48 and the mean score was(33.78±8.16).And 500 subjects,according to 85.03%,were with scores over 24,which meant positive affection was predominated.Female,mateless elderly,elderly with chronic disease,low education,low income,poor parent-child relationship,higher cognitive level and low social support level were with significantly poorer psychological health status(P0.05).Mateless elderly,elderly with low education,low income,with chronic disease,living alone,poor parent-child relationship,low cognitive level,poor live ability and low social support level were with significantly lower level of subjective well-being(P0.05).Total score of SCL-90,mean score of positive symptoms,score of factors involving somatization,diet and sleep and depression were negatively associated with total score of MUNSH(P0.05),while were positively associated with negative affection and negative experience(P0.05).The descending order of SCL-90 various factors,predicting subjective well-being,was diet and sleep,anxiety,somatization and depression respectively.Conclusion Psychological health status might be strongly associated with subjective well-being,in which diet and sleep,anxiety,somatization and depression could predict subjective well-being rightly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".