Deaf older adults’ experiences of support from a mobile old-age care team providing support in Swedish sign language
Bibliographic record
Abstract
INTRODUCTION: To address communication barriers, minimise social isolation, prevent psychosocial illness and increase the independence of Deaf older adults, a mobile care team consisting of Deaf assistant nurses using sign language was initiated and developed by a nongovernmental organisation in a region in southern Sweden. AIM: To describe Deaf older adults’ experiences receiving support from an NGO-initiated mobile old-age care team for Deaf and sign language-speaking older adults in Sweden. METHODS: A series of 15 individual interviews with four Deaf older adults were analysed via content analysis. RESULTS: Support from the mobile care team was appreciated, as illustrated by the following categories: support in everyday activities, communication supported and enabled and support for psychosocial well-being. The care team facilitated communication using sign language. For example, they enabled in-depth communication and information sharing and supported older adults in expressing opinions and thoughts to authorities and regular care staff. Increased communication supported psychosocial well-being, independence, and feelings of safety. CONCLUSION: A sign language mobile care team that is well familiar with Deafness as a culture rather than a hearing disability is highly valued by Deaf older adults in need of home or residential care later in life. It also shows that access to a sign language mobile care team leads to increased psychological wellbeing and happiness among Deaf older adults, as well as to their increased participation in decision-making concerning various aspects of their lives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".