Social Participation as a Predictor of Morbid Thoughts and Suicidal Ideation among the Elderly Population: A Cross-Sectional Study on Four Low-Middle-Income Countries
Bibliographic record
Abstract
Social wellbeing constitutes a critical aspect of one’s health, quality of life, and overall psychosocial wellbeing. Social isolation and perceived loneliness are growing public health concerns as they are considered to be important risk factor for poor physical and mental health outcomes. Not much is known about how the level of one’s social participation is associated with morbid thought and suicidal ideation. In the present study, we aimed to investigate whether social participation shows any significant correlation with morbid thought and suicidal ideation among the elderly population. Methods: Cross-sectional data were collected from Wave 1 of the Study of Global AGEing and Adult Health (SAGE). The sample population consisted 2018 men and women aged 65 years and above from the following countries: China (n = 787), Ghana (n = 278), India (n = 560), and Russia (n = 396). Outcome variables of self-reported occurrence of morbid thoughts and suicide ideation during the past 12 months were reported. Results: A great majority of the participants reported not participating in activities such as public meetings (84.6%), club meeting (49.6%), neighborhood activities (46%), and religious activities (57.2%). Those who reported attending public meetings several times a year had a higher likelihood of reporting having morbid thoughts (predicted probability = 1.24, 95% CI = 1.02, 1.52). However, the association was no longer significant after stratifying by sex. Attending clubs (marginal effect = 0.61, 95% CI = 0.49, 0.76) and neighborhood activities (predicted probability = 0.71, 95% CI = 0.58, 0.88) several times a year showed protective effects against morbid thoughts. Being visited by friends several times a month (predicted probability = 0.52, 95% CI = 0.40, 0.67) and visiting friends (predicted probability = 0.61, 95% CI = 0.50, 0.75) several times a year also showed lower likelihood of morbid thoughts. Similar effects were observed for attending social gatherings with colleagues and social events as well. Conclusions: The present findings suggest that there exist significantly positive associations between participation in social activities and morbid thoughts and suicidal ideation among the elderly population in the sample countries. More in-depth studies are necessary to investigate the barriers to participation in social activities as well as the role of the quality of social relationships with experiencing suicidal thoughts.
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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 | 0.001 | 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) | 0.000 | 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".