The Association between Health Beliefs and Fall-Related Behaviors and Its Implication for Fall Intervention among Chinese Elderly
Bibliographic record
Abstract
To apply the Health Belief Model (HBM) to fall prevention of the elderly and estimate fall health beliefs and their relationships with fall-related behaviors, a citywide cross-sectional study was conducted among people aged 60 years or over in 13 out of 16 districts in Shanghai, China, in September 2018. A total of 5833 participants were investigated. Of this, 43.4% were male; 48.8% were aged 60–69; 18.1% were uneducated; and 50.3% were living in urban areas. People who were older, less educated, living in rural areas generally had lower scores in the 7 HBM dimensions and also had lower proportions of fall risk-reduction behaviors, except that the less educated elderly were more likely to participate in exercise and training and the rural elderly were more likely to check house environment and participate in exercise and training (p < 0.001). The HBM dimensions were generally positively correlated with the risk-reduction behaviors except that “perceived severity” was negatively correlated with four risk-reduction behaviors and behavior number, “cues to action” was negatively correlated with purchasing shoes, and “perceived benefits” was negatively correlated with participating in exercise activities and fall prevention training (p < 0.05). When HBM is applied in the field of fall prevention, the interpretation of the results of each dimension has its characteristics in the fields of injury research. Fall prevention strategies should focus on improving the health beliefs and behaviors in those who were older, less educated and living in rural areas, implementing different levels of fall prevention activities to meet different needs, improving the accessibility and applicability of related resources, and raising the organizational level of related fall prevention activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".