Widowhood and depression: a longitudinal study of older persons in rural China
Bibliographic record
Abstract
Using six waves of longitudinal data (2001-2015) collected in Anhui, China (N = 2,131) and generalized estimating equations (GEE) models, this study fulfilled several objectives. First, the study compared the widowed to the married to examine if the transition to and duration of widowhood contributes to changes in depression. Second, the study examined if the bereavement-depression relationship is a process that precedes widowhood or is an abrupt change following the death of a spouse. Third, the study examined if social resources influence the bereavement-depression relationship. The study found that there is pre-widowhood effect on depression and that the widowhood event also contributes to increases in depression. Levels of post-widowhood depressive symptoms peak during the first six months bereavement and taper off within 25 months. Controlling for social support, contact with children, and living arrangements does not change the bereavement-depression relationship. The findings support Attachment Theory, which suggests that the loss of a spouse leads to emotional isolation that cannot be overcome with kin-based social support and social integration.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".