Improving delivery of primary care for vulnerable migrants: Delphi consensus to prioritize innovative practice strategies.
Bibliographic record
Abstract
OBJECTIVE: To identify and prioritize innovative strategies to address the health concerns of vulnerable migrant populations. DESIGN: Modified Delphi consensus process. SETTING: Canada. PARTICIPANTS: Forty-one primary care practitioners, including family physicians and nurse practitioners, who provided care for migrant populations. METHODS: We used a modified Delphi consensus process to identify and prioritize innovative strategies that could potentially improve the delivery of primary health care for vulnerable migrants. Forty-one primary care practitioners from various centres across Canada who cared for migrant populations proposed strategies and participated in the consensus process. MAIN FINDINGS: The response rate was 93% for the first round. The 3 most highly ranked practice strategies to address delivery challenges for migrants were language interpretation, comprehensive interdisciplinary care, and evidence-based guidelines. Training and mentorship for practitioners, intersectoral collaboration, and immigrant community engagement ranked fourth, fifth, and sixth, respectively, as strategies to address delivery challenges. These strategies aligned with strategies coming out of the United States, Europe, and Australia, with the exception of the proposed evidence-based guidelines. CONCLUSION: Primary health care practices across Canada now need to evolve to address the challenges inherent in caring for vulnerable migrants. The selected strategies provide guidance for practices and health systems interested in improving health care delivery for migrant populations.
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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 | 0.001 | 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 | 0.000 |
| 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".