Self-reported dual sensory impairment and related factors: a European population-based cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Data on population-based self-reported dual vision and hearing impairment are sparse in Europe. We aimed to investigate self-reported dual sensory impairment (DSI) in European population. METHODS: A standardised questionnaire was used to collect medical and socio-economic data among individuals aged 15 years or more in 29 European countries. Individuals living in collective households or in institutions were excluded from the survey. RESULTS: Among 296 677 individuals, the survey included 153 866 respondents aged 50 years old or more. The crude prevalence of DSI was of 7.54% (7.36-7.72). Among individuals aged 60 or more, 9.23% of men and 10.94% of women had DSI. Eastern and southern countries had a higher prevalence of DSI. Multivariable analyses showed that social isolation and poor self-rated health status were associated with DSI with ORs of 2.01 (1.77-2.29) and 2.33 (2.15-2.52), while higher income was associated with lower risk of DSI (OR of 0.83 (0.78-0.89). Considering country-level socioeconomic factors, Human Development Index explained almost 38% of the variance of age-adjusted prevalence of DSI. CONCLUSION: There are important differences in terms of prevalence of DSI in Europe, depending on socioeconomic and medical factors. Prevention of DSI does represent an important challenge for maintaining quality of life in elderly population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".