Evaluation of evidence-based literature and formulation of recommendations for the clinical preventive guidelines for immigrants and refugees in Canada
Bibliographic record
Abstract
BACKGROUND: This article describes the evidence review and guideline development method developed for the Clinical Preventive Guidelines for Immigrants and Refugees in Canada by the Canadian Collaboration for Immigrant and Refugee Health Guideline Committee. METHODS: The Appraisal of Guidelines for Research and Evaluation (AGREE) best-practice framework was combined with the recently developed Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to produce evidence-based clinical guidelines for immigrants and refugees in Canada. RESULTS: A systematic approach was designed to produce the evidence reviews and apply the GRADE approach, including building on evidence from previous systematic reviews, searching for and comparing evidence between general and specific immigrant populations, and applying the GRADE criteria for making recommendations. This method was used for priority health conditions that had been selected by practitioners caring for immigrants and refugees in Canada. INTERPRETATION: This article outlines the 14-step method that was defined to standardize the guideline development process for each priority health condition.
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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 | 0.143 | 0.402 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".