Bibliographic record
Abstract
China is witnessing several major demographic trends (declining fertility, population aging, rural to urban migration) that are likely to intersect in unique and significant ways to influence the health and well-being of its older adult population. Concerns that such trends may be eroding traditional family structures and values raise questions about the continued importance of children in the lives of their older parents. Do children matter and, if so, what is it about having children that makes a difference to the mental health of their parents? This study addressed these issues using data from the 2014 Chinese Longitudinal Aging Social Survey (N= 11,511 individuals aged 60+). Multivariate regression analyses revealed the importance of having one or two children for parental mental health. Although the sex composition of the children did not have an impact, the children’s achievements, the support they provided, and the parents’ attitudes toward filial piety directly influenced parental depression levels. However, only children’s achievements and the support they provided to parents mediated the relationship between the number of children and depression. These findings support the view that children continue to be important to the mental health of their older parents in contemporary China. Whereas male preference may have become less relevant, traditional values around filial piety continue to play a role. Yet, what appears to matter most when it comes to the influence that children have on parents’ mental health are their perceived accomplishments in life and their meaningful presence in the day-to-day lives of their parents.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".