Patient Engagement: How It Can Help Immigrant Women Navigate Their Healthcare in Canada
Bibliographic record
Abstract
Background: Despite patient engagement becoming mainstream, the literature suggests that immigrants tend to be less engaged in their care. The power differential between patients and providers seems to be much wider for immigrants. Especially immigrant women across age and social positions often experience multiple barriers to healthcare. These obstacles are mostly due to the social determinants of health, often linked to language, ethnicity, culture, religion, limited access to services and so on. This community-based research (CBR) study aims to understand how immigrant women perceive the notion of "patient engagement", and how their experiences with health services in Saskatoon have shaped it. Methods: This patient-orientated study is participatory in nature where immigrant women and patient/family advisors are active members of the research team. A semi-structured interview was codesigned, pilot tested, and implemented to distill experiences and perspectives from immigrant women of diverse backgrounds in Saskatoon. Results: Based on the interviews (n= 25), different patterns, similarities and/or differences have been emerging from the perspectives shared by immigrant women. Regardless of their differences in background, the participants appeared to share somewhat similar experiences with healthcare, particularly with navigating within the Canadian healthcare system and struggling with the language barrier. Conclusion: In any CBR study, the process is as important as the outcome. At the end of this study, a patient engagement model will be co-developed along with the community members to ensure it is appropriate for cross-cultural patients and reflects participants’ perspectives.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".