Digital Pathways to Reducing Depression Among Aging Populations Through the “Broadband China” Pilot Program: Quasi-Natural Experiment
Bibliographic record
Abstract
BACKGROUND: With the rise of digital technology, infrastructure development has become vital for social welfare and public health. However, evidence on its effects on depressive symptoms among middle-aged and older adults remains limited. OBJECTIVE: This study evaluates the impact of digital infrastructure development on depressive symptoms among middle-aged and older adults, focusing on underlying mechanisms, heterogeneous effects, and health inequalities. METHODS: We use longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), 2011-2020 (N=56,211). Exploiting the quasi-natural experiment of the "Broadband China" pilot policy, we apply a difference-in-differences approach to estimate the effect on depressive symptoms. Mediation analysis follows the Baron-Kenny 3-step procedure, with bootstrap tests (95% CI) for robustness, and causal interpretation relies on standard assumptions for observational data. Subgroup analyses explore heterogeneity across age, education, and sex groups. RESULTS: Our findings indicate that the "Broadband China" pilot significantly reduces depressive symptoms among middle-aged and older adults (-0.33, P<.01). The positive effect is primarily mediated through strengthened social networks, including increased family connection, close social interactions, and greater social participation. Heterogeneity analysis shows that the benefits for depression reduction are more pronounced among women (-0.38, P<.01), middle-aged adults (-0.41, P<.01), and those with lower levels of education (-0.33, P<.01). Moreover, the results suggest that digital infrastructure plays a compensatory role in mitigating health disparities, thereby reducing inequalities in depression outcomes (-0.01, P<.01). CONCLUSIONS: Digital infrastructure reduces depressive symptoms among aging populations mainly by strengthening social networks. Embedding infrastructure into long-term strategies, enhancing digital literacy, and integrating digital health services are key to promoting healthy aging and reducing inequalities.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".