Understanding racialised older adults’ experiences of the Canadian healthcare system, and codesigning solutions: protocol for a qualitative study in nine languages
Bibliographic record
Abstract
INTRODUCTION: Racialised immigrant older adults (RIOAs) in Canada have poorer self-rated health and are more likely to report chronic conditions, while they concurrently experience well-documented challenges in navigating and accessing the healthcare system. There is strong evidence that patient and caregiver engagement in their healthcare leads to improved management of chronic disease and better health outcomes. International research suggests that engagement has the potential to reduce health disparities and improve quality of care. We aim to (1) describe what role(s) RIOAs are/are not taking in their own healthcare, from the perspectives of participant groups (RIOAs, caregivers and healthcare providers (HCPs)); and (2) develop a codesign process with these participants, creating linguistically aligned and culturally aligned tools, resources or solutions to support patient engagement with RIOAs. METHODS AND ANALYSIS: Using a cross-cultural participatory action research approach, our work will consist of three phases: phase 1, strengthen existing partnerships with RIOAs and appropriate agencies and cultural associations; phase 2, on receipt of informed consent, in-depth interviews with RIOAs and caregivers (n=~45) and HCPs (n=~10), professionally interpreted as needed. Phase 3, work with participants, in multiple interpreted sessions, to codesign culturally sensitive and linguistically sensitive/aligned patient engagement tools. We will conduct this research in the Waterloo-Wellington region of Ontario, in Arabic, Bangla, Cantonese, Hindi, Mandarin, Punjabi, Tamil and Urdu, plus English. Data will be transcribed, cleaned and entered into NVivo V.12, the software that will support team-based analysis. Analysis will include coding, theming and interpreting the data, and, preparing narrative descriptions that summarise each language group and each participant group (older adults, caregivers and HCPs), and illustrate themes. ETHICS AND DISSEMINATION: Ethics clearance was obtained through the University of Waterloo Office of Research Ethics (ORE #43297). Findings will be disseminated through peer-reviewed publications, presentations and translated summary reports for our partners and participants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".