Prevalence of Individuals With Deafblindness and Age-Related Dual-Sensory Loss
Bibliographic record
Abstract
Introduction The authors of this paper have compiled a report on the prevalence of deafblindness and dual-sensory loss based on the review of existing estimates. The purpose is to inform readers of the importance of using consistent, well-researched definitions and survey questions in future prevalence studies. Methods Articles were extracted through ProQuest and EBSCOhost, online library databases of Cambrian College and Laurentian University. Keywords search included “deafblindness,” “dual-sensory impairment,” “dual-sensory loss,” “age-related,” “congenital,” “acquired,” and “prevalence.” Additionally, the authors conducted a search with Google for research reports and Google Scholar for other relevant peer-reviewed articles. Results This review provides a current overview of prevalence estimates of deafblindness and age-related dual-sensory loss around the world, examining 19 articles or reports published over the last 20 years (2000–2020) in 18 countries, including the European Union (consisting of 8 countries). In line with the prevalence estimates by the World Federation for the Deafblind global report 2018, the review indicates an estimated 0.2–2% prevalence of dual-sensory impairment and underscores varying ranges of prevalence among populations, studies or countries, age groups, and types of deafblindness. The review highlights that the prevalence of deafblindness or dual-sensory loss was often not comparable across studies, but it is clear that the prevalence of dual-sensory impairment increases with age. The studies varied in methods (e.g., population surveys, cross-sectional, and longitudinal studies). Implication for Practitioners The review provides evidence of varying ranges of prevalence rates. Future prevalence studies may benefit from consistent definitions, standard data-collection tools to do better comparisons across countries, and identify factors that predict higher or lower prevalence rates among populations and age groups.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".