Development of guidelines for recently arrived immigrants and refugees to Canada: Delphi consensus on selecting preventable and treatable conditions
Bibliographic record
Abstract
BACKGROUND: Setting priorities is critical to ensure guidelines are relevant and acceptable to users, and that time, resources and expertise are used cost-effectively in their development. Stakeholder engagement and the use of an explicit procedure for developing recommendations are critical components in this process. METHODS: We used a modified Delphi consensus process to select 20 high-priority conditions for guideline development. Canadian primary care practitioners who care for immigrants and refugees used criteria that emphasize inequities in health to identify clinical care gaps. RESULTS: Nine infectious diseases were selected, as well as four mental health conditions, three maternal and child health issues, caries and periodontal disease, iron-deficiency anemia, diabetes and vision screening. INTERPRETATION: Immigrant and refugee medicine covers the full spectrum of primary care, and although infectious disease continues to be an important area of concern, we are now seeing mental health and chronic diseases as key considerations for recently arriving immigrants and refugees.
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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 | 0.183 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".