A follow-up study on social support to the elderly in Xicheng district of Beijing
Bibliographic record
Abstract
Objective To understand the status of social support to the elderly in Xicheng district of Beijing,and to explore the related factors and effective intervention method,so as to provide theoretical basis for improving their mental health.Methods A total of 2342 seniors aged 60-80 years were randomly selected using stratified cluster sampling method.All subjects were surveyed and assessed using Memorial University of Newfoundland Scale of Happiness(MUNSH),Social Support Rating Scale(SSRS),Self-Rating Anxiety Scale(SRAS)and Self-rating Depression Scale(SDS).They were randomly divided into control group and trial group.Subjects in the trial group received social intervention as health education,community support,psychological consultation and group discussion.Results The total score of social support was 39.67(6.59).Objective support score was 9.47(2.47)and subjective support score was 22.90(4.23).Out of 2342 seniors surveyed,only 19.2% received high level of social support,majority of them received medium level,but still there was 18.1% of seniors who obtained low level of social support.The total support score was significantly related to marriage,job,family type and relationship,watching TV,reading,breeding pet,singing and dancing,personality,life quality,happiness,anxiety and depression(P0.05).Scores of each factor between two groups had no significant difference before intervention.However,after intervention,the scores of objective and subjective support,as well as total score of support were much higher compared to those before intervention(P0.05).Conclusions Social support is independently associated with factors as life quality,happiness,anxiety and depression.It is suggested that mental health intervention should be applied to seniors with low social support.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".