Bridging gaps: the role of eHealth tools in immigrant health care transitions
Bibliographic record
Abstract
In 2022, Canada saw a significant influx of immigrants and non-permanent residents who despite contributing significantly to the economy, continue to face substantial health care challenges, including a decline in the “healthy immigrant effect”. This study examines the role of eHealth tools in facilitating immigrants’ health care transition in Canada, addressing a critical gap in existing research. Using a scoping review methodology, relevant literature was identified through the Web of Science, Scopus, and ProQuest databases, Ovid platform, and Google Scholar search engine. Twelve studies published between 2021 and 2024 met all criteria for inclusion. Thematic analysis was conducted using NVivo, Google NotebookLM, and DeepSeek. The findings highlight the potential of eHealth tools in chronic disease management, mental health support, and primary care access while also exposing disparities in eHealth adoption due to systemic barriers, limited digital literacy, and lack of awareness of existing solutions. The study underscores the need for culturally inclusive eHealth solutions, targeted digital literacy programs, and improved access to health care information. It calls for policy interventions and further research to ensure equitable health care access for Canada’s growing immigrant population and facilitate their integration into the health care system.
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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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".