Primary care access barriers faced by immigrant populations in their host countries: a systematic review protocol aiming to construct a conceptual framework using root cause analysis to capture ‘what leads to what’
Bibliographic record
Abstract
INTRODUCTION Understanding primary care access or health service utilisation challenges among immigrant communities is important for tailoring services to community needs, which is the core of precision population health. AIM We aim to inventory the primary care access barriers faced by immigrant communities through a comprehensive systematic review and develop a conceptual framework to explain the barriers, using a root cause analysis approach. METHODS Academic databases of primary research articles and grey literature will be searched using appropriate keywords. Relevant information will be extracted into tabular format from finally selected literature. Our proposed approach of framing the barriers to identify the root causes is adapted from the root cause analysis method, which is the process of identifying and understanding the underlying causes to discover the root causes of problems. RESULTS The study will produce a systematic, quantified and documented list of the barriers faced by immigrants in a solution-oriented approach. DISCUSSION The proposed research, as a first step towards determining possible mitigation strategies for health-care access by immigrants, will provide the background needed to devise and test tailored interventions to improve future access to health care for immigrants. We will follow the integrated knowledge translation or community engagement knowledge mobilization approach, where we are engaged with community-based citizen researchers from the inception of our programme. We plan to disseminate the results of our review through meetings with key stakeholders and social media outreach, followed by journal publications and presentations on relevant platforms.
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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 | 0.200 | 0.169 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".