Scoping review protocol: what is the state of evidence for the use of communication apps with immigrant seniors in long-term care and community settings?
Bibliographic record
Abstract
Introduction First language care is critical for older immigrant adults with limited English proficiency, especially in long-term care settings where most residents require staff assistance and experience complex chronic conditions, resulting in multiple communication interactions where language poses a barrier. Although there are a myriad of cultural-language translation apps and devices available, there is a gap in both research and practice on the acceptability and feasibility of these digital resources within the context of long-term care and community settings for older immigrant adults, from a cultural relevance and digital health equity perspective. Our paper outlines a scoping review protocol to examine the state of the literature on the extent to which cultural-language translation apps are used in long-term care settings and community-based elder care. We will also examine the extent to which such apps bridge or further gaps in equitable, accessible and acceptable care for older immigrant adults with limited English language proficiency. Methods and analysis This scoping review protocol will employ an adapted five-stage framework outlined by Arksey and O’Malley guided by enhancements recommended by Levac et al and Colquhoun et al . Using the Joanna Briggs Institute’s population, concept and context framework, we defined the scope of the scoping review by identifying the target population, concepts for investigation and the context within which the research is situated. We will conduct a search of the literature from 2005 to 2024 using five bibliographic databases from health sciences (Healthstar OVID, MEDLINE OVID and Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO), engineering (Engineering Village Elsevier) and a cross-disciplinary database (Web of Science Clarivate). The research team will adopt a critical, equity-focused approach for the scoping review by integrating Richardson et al ’s framework for Digital Health Equity into our analysis of the findings. This will ensure that health and social equity perspectives are integrated within our methodology and analytical lens. Our analysis will specifically examine selected studies for their engagement with health equity and their ability to address issues such as ageism, ableism and the digital divide within geriatric care. Ethics and dissemination Ethics approval is not required for this scoping review as it involves secondary analysis of published works and no primary data collection involving human subjects. Findings of the review will be shared with community partners and disseminated through publications, conferences and peer-reviewed publications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".