Gender Differences in the Digital Divide, Digital Back-Feeding, and Health-Related Quality of Life Among Rural Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The digital divide has loomed as a global public issue in recent years. However, evidence is limited regarding whether the digital divide is associated with health-related quality of life (HRQOL) and whether digital back-feeding would buffer this association. OBJECTIVE: This study aims to explore the role of digital back-feeding in the relationship between the digital divide and HRQOL among older men and women living in rural China. METHODS: We used data from wave 3 of the Shandong Rural Elderly Health Cohort, conducted in 2022. A total of 3242 (n=1946, 60.02% women) rural older adults were included in the analysis. Moderating effect analysis was performed using Tobit regression models and margins plots. RESULTS: A total of 71.01% (2302/3242) of the participants reported experiencing digital divide. Participants experiencing digital divide were significantly associated with lower HRQOL as measured by EQ-5D-5L scores (β=-0.020; P<.001). We found that digital back-feeding buffered the relationship between digital divide and HRQOL (β=0.024; P=.02). Furthermore, gender-stratified analyses revealed divergent moderation patterns; a significant buffering role was observed in women (β=0.031; P=.02), whereas no substantially significant moderating role emerged in men. CONCLUSIONS: Our study established a significant inverse association between the digital divide and HRQOL among rural adults. Digital back-feeding emerged as a measurable protective buffer mitigating this adverse relationship. Furthermore, this buffering effect was only observed among older women. Policy implications underscore the necessity of gender-tailored digital inclusion strategies, particularly advocating for technology-proficient adult offsprings to prioritize digital engagement with their mothers in digitally marginalized rural communities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".