Factors influencing self-help education during public emergencies among older migrants: A cross-sectional study in china's mainland
Bibliographic record
Abstract
Background The number of older adult migrants in China continues to grow. As a unique population characterized by both “mobility” and “aging,” they face heightened risks during public emergencies. Objective This study investigated the current acceptance rate among these older adult migrants with respect to education for self-help in a public emergency (ESHPE) and analyzed influencing factors. Study design A cross-sectional study. Methods This study’s data were derived from the 2018 National Migrant Population Dynamic Monitoring Survey, conducted by the National Health Commission of China; overall, 5840 migrants were included in this study. SPSS 25.0 and RStudio 4.3.2 were utilized to analyze the selected sample, while Chi-square tests were conducted to perform univariate analysis on the acceptance rate of ESHPE among older adult migrants. A combination of the Random Forest model and binary logistic regression analysis was employed to assess the importance of statistically significant variables. Results Overall, 1162 older adult migrants received ESHPE, representing an acceptance rate of 19.90 %. The acceptance rate was lower among those aged over 75 (Odds Ratio [OR] : 0.637, 95 % Confidence Interval [CI] : 0.454–0.893); residing in rural villages (OR : 0.757, 95 % CI : 0.616–0.931); with a migration duration of 11–15 years (OR : 0.679, 95 % CI : 0.540–0.853), 16–20 years (OR : 0.725, 95 % CI : 0.547–0.961), or over 20 years (OR : 0.708, 95 % CI : 0.531–0.943); who had migrated for family (OR : 0.646, 95 % CI : 0.544–0.768), social (OR : 0.559, 95 % CI : 0.434–0.718), or other reasons (OR : 0.364, 95 % CI : 0.191–0.691); and who had not established resident health records (OR : 0.693, 95 % CI : 0.582–0.825) or were unaware of or unclear about such records (OR : 0.494, 95 % CI : 0.388–0.630). Conclusions The acceptance rate of ESHPE in this cohort remains relatively low. Therefore, targeted intervention measures tailored to their specific needs must be developed, and more focused educational resources for public emergencies must be created. Online interactive platforms should be established to enhance the self-help education content and strategies. Such measures should help improve the acceptance rate of ESHPE among older adult migrants.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".