Social determinants of health and dual sensory loss in older adults: A scoping review
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDH), such as socioeconomic status, education, healthcare access, and social support, play a significant role in shaping individuals lived experiences. Dual sensory loss (DSL), a distinct disability involving both vision and hearing loss, poses greater challenges for daily living compared to the general population. This scoping review synthesized evidence on how various SDH indicators influence the life experiences of older adults with DSL. METHODS: Five scientific databases were searched from January 2014 to May 2024. Articles focusing on individuals aged 60 and older with DSL, in the context of at least one SDH indicator, were included. RESULTS: A total of 69 studies met the eligibility criteria. Most studies addressed the following SDH indicators: disability (n = 46), social inclusion and non-discrimination (n = 21), gender (n = 10), and access to healthcare services (n = 9). Disability-related indicators revealed higher risks of mobility limitations, cognitive decline, depression, anxiety, social isolation, and workplace discrimination, all adversely affecting mental health and quality of life. Older adults with DSL encounter significant barriers to accessing healthcare, such as absence of adequate assistive devices, communication challenges, and high healthcare costs. Many report dissatisfaction with the quality of care received. CONCLUSIONS: Our review identifies disparities that increase the vulnerability of older adults with DSL and restrict their access to healthcare, rehabilitation, and social participation. These findings warrant further research on underexplored SDH factors, using robust data sources that collects information on the lived experiences of older adults with DSL. Addressing these social determinants requires a comprehensive approach, including raising awareness, improving service access, enhancing social support networks, and ensuring inclusive policies and practices.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".