Language needs and service provision for older persons from culturally and linguistically diverse backgrounds in south‐east Melbourne residential care facilities
Bibliographic record
Abstract
Objectives: To provide up‐to‐date figures on the language needs of older persons from culturally and linguistically diverse (CALD) backgrounds in local residential care facilities and to investigate the extent to which these needs are catered for by the provision of language‐relevant services. Methods: A postal questionnaire was sent to 189 registered aged care facilities in the south‐east region of Melbourne, Victoria. The questionnaire focused on three main issues: the number of residents who preferred or needed to speak non‐English languages; the staff available to speak to residents in non‐English languages; and the language‐specific services provided at their facility. Results: As many as 19% (1191/6409) of residents either preferred or needed to speak one of 40 different non‐English languages. While over half of the facilities had at least one staff member who conversed with residents in their preferred language, residents speaking nine non‐English languages were never spoken to in their original tongue. Almost one‐quarter of the facilities did not provide any language‐relevant services. Conclusion: The findings emphasise the need for widespread use of language‐appropriate services and, due to the growing ageing migrant population, have important policy implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".